Wearable device for reducing exposure to pathogens of possible contagion

ABSTRACT

A wearable device for reducing exposure to pathogens of possible contagion includes a detection component configured to detect a proximate subject in relation to a user wherein the user is in possession of the wearable device, a data input component configured to capture data relating to the proximate subject, wherein the data relating to the currently proximate subject further comprises an inoculation status of the currently proximate subject, a processor configured to calculate a degree of transmission threat between the user and the proximate subject, wherein calculating the degree of transmission threat further comprises identifying a pathogen of possible contagion, locating a reproduction rate for the pathogen of possible contagion, calculating the degree of transmission threat as a function of the data input component and the reproduction rate, and a user-signaling component configured to generate an output as a function of the degree of transmission threat.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Non-provisional application Ser.No. 16/949,088 filed on Oct. 13, 2020 and entitled “WEARABLE DEVICE FORREDUCING EXPOSURE TO PATHOGENS OF POSSIBLE CONTAGION,” the entirety ofwhich is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to the field of infectiousdiseases. In particular, the present invention is directed to a wearabledevice for reducing exposure to pathogens of possible contagion.

BACKGROUND

Uncertainty surrounding safety of public spaces and shared locations canbe challenging. Existing challenges fail to adequately providereassurances surrounding interactions with strangers, locations, and/orvendors.

SUMMARY OF THE DISCLOSURE

In an aspect, a wearable device for reducing exposure to potentialdangers includes a detection component configured to: perform a baselinelocation measurement of a location containing a currently proximatesubject, and detect the currently proximate subject in relation to auser, wherein the user is in possession of the wearable device, a datainput component configured to capture data relating to the currentlyproximate subject, a processor configured to calculate a degree oftransmission threat between the user and the currently proximate subjectas a function of the data relating to the currently proximate subject,and a user-signaling component configured to generate an output as afunction of the degree of transmission threat, wherein generating theoutput further comprises: identifying a protected location, andnavigating to the protected location.

In an aspect, a method of reducing exposure to potential dangersincludes performing, by a wearable device, a baseline locationmeasurement of a location containing a currently proximate subject,detecting by the wearable device, a currently proximate subject inrelation to a user wherein the user is in possession of the wearabledevice, capturing by the wearable device, data relating to the currentlyproximate subject, wherein the data relating to the currently proximatesubject further comprises a distance relating to the currently proximatesubject and the user in possession of the wearable device, calculatingby the wearable device, a degree of transmission threat between the userand the currently proximate subject, wherein calculating the degree oftransmission threat further comprises: calculating the degree oftransmission threat as a function of the data relating to the currentlyproximate subject, and generating by the wearable device, an output as afunction of the degree of transmission threat, wherein generating anoutput further comprises: identifying a protected location, andnavigating to the protected location.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of awearable device for reducing exposure to pathogens of possiblecontagion;

FIG. 2 is a block diagram illustrating an exemplary embodiment of a userdatabase;

FIG. 3 is a block diagram illustrating an exemplary embodiment of aproximate subject database;

FIG. 4 is a block diagram illustrating an exemplary embodiment of apathogen database;

FIG. 5 is a block diagram illustrating an exemplary embodiment of areproduction database;

FIG. 6 is a block diagram illustrating an exemplary embodiment of amachine-learning component;

FIGS. 7A-B are diagrammatic representations of exemplary embodiments ofdetection component;

FIGS. 8A-C are diagrammatic representations of exemplary embodiments ofsignaling component;

FIG. 9 is a process flow diagram illustrating an exemplary embodiment ofa method of reducing exposure to pathogens of possible contagion;

FIG. 10 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations, and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

Embodiments disclosed herein harness detection technology and opt-inprotocols to identify relatively safe navigational routes and locationsfor users with regard to pathogen exposure. At a high level, aspects ofthe present disclosure are directed to a wearable device for reducingexposure to pathogens of possible contagion by determining localizeddata relating to infection rates and inoculation statuses. In anembodiment, a proximate subject is detected, and data is capturedrelating to the proximate subject. A degree of transmission threat iscalculated by a wearable device by identifying a pathogen of possiblecontagion and locating a “localized” reproduction rate for the pathogenof possible contagion and the user's personal health profile. Auser-signaling component generates an output as a function of a degreeof transmission threat. An output may alert a user as to a potentiallydangerous situation.

Referring now to the drawings, FIG. 1 illustrates an exemplaryembodiment of a wearable device for reducing exposure to pathogens ofpossible contagion. A “wearable device,” as used in this disclosure, isan electronic device that may be worn and/or to a user in closeproximity or within visual and or audible range. A wearable device 104may include an electronic device that may detect, analyze, and/ortransmit information concerning a user, and may be configured to providefeedback for the user. Furthermore, the device may acquire informationfrom the target site at an individual level as well as swarm level, inaddition, the wearable device can calculate distances and proximity toother individuals as well a swarm or multiple swarms of individuals. Awearable device 104 may make contact and/or touch one or more body partsof a user, including but not limited to the fingers, palm, wrists,chest, forearms, ears, eyes, ear canal, forehead, temple, back, foot,ankle, and the like. In an embodiment, a wearable device 104 may notmake contact and/or touch one or more body parts of a user, but rathermay be in the vicinity and/or located adjacent to a user, such as acomputer and/or mobile phone, as described below in more detail. Awearable device 104 may be worn as an accessory, embedded in clothingworn by a user, implanted on a user's body, and/or tattooed onto auser's skin. For instance and without limitation, a wearable device 104may include a smartwatch, a fitness tracker, a sports watch, ahead-mounted display, a virtual reality headset, smart glasses,hearables, smart jewelry, smart clothing, hearing aids, and the like. Awearable device 104 may include any electrical equipment controlled by acentral processing unit (CPU) such as but not limited to a laptopcomputer, a desktop computer, a smartphone, a mobile device, acomputerized medical device, a tablet, and the like. A wearable device104, may be operated by a user 108. A wearable device 104 may include aservice, such as but not limited to a software and/or hardware module. Awearable device 104 may include and/or interact with a cloud basedand/or internet of things (JOT) based application, including but notlimited to infrastructure as a service (IaaS), platform as a service(PaaS), software as a service (Saas), mobile backend as a service(MBaaS), serverless computing, function as a service (FaaS) and thelike. In an embodiment, cloud based services may include but are notlimited to a private cloud, a public cloud, a hybrid cloud, a communitycloud, a multi-cloud, a poly cloud, a big data cloud, a high-performancecomputing (HPC) cloud and the like. This may include sensor networkslocated in commercial buildings, people count, airflow, temperaturefluctuations, sanitation (bathrooms etc.), elevator occupancy and thelike. This may include office and/or home appliances, thermostats,and/or humidistats. A “user,” as used in this disclosure, is a humansubject.

With continued reference to FIG. 1, a wearable device 104 may interactwith a user and/or a remote device using local wireless communicationssuch as but not limited to Bluetooth wireless technology for datatransfer and exploiting RSSI levels to determine distances of anindividual and or swam, and density of the crowds. Optical camerassensors can be used to determine distances either from known physicalboundaries locations as well as from analyzing the local area that theindividual is in. Furthermore, Magnetic Induction, wireless local areanetwork (WLAN), Wi-Fi, near field communications (NFC), wiredcommunications such as wired Universal Serial Bus (USB), and the like.In an embodiment, wearable device 104 may be configured to communicatewith remote device using on a Device-to-device (D2D) or thought aDevice-to-many architecture using any network and/or any networkinterface device methodology as described herein. Wearable device 104may be configured to communicate wirelessly over a network, includingany network as described herein. Examples of a network interface deviceinclude, but are not limited to, a network interface card (e.g., amobile network interface card, a LAN card), a modem, and any combinationthereof. Examples of a network include, but are not limited to, a widearea network (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer, a computing device,and/or a wearable device 104.

With continued reference to FIG. 1, wearable device 104 includes adetection component 112. A “detection component,” as used in thisdisclosure, is a component configured to detect a currently proximatesubject 116 in relation to a user 108, wherein the user is in possessionof the wearable device 104. A “currently proximate subject,” as used inthis disclosure, is a human subject, an animal, material or surface,location, setting, surroundings, physical location, and the like,located in an area near a user 108. A currently proximate subject 116may include a human subject, an animal, material or surface, residuethat may be located within a certain distance, mobile device and/orspecified location of a user 108. In addition, system may providefeedback as the user is getting closer to the currently proximatesubject updating the user from time to time or alerting the user withina specified distance of the currently proximate subject. Feedback can bedelivered to the user using sonification, visually with graphics, ortext, haptic or photonic. This may include any public and/or privatelocation. For instance, and without limitation, a currently proximatesubject 116 may be seated or projected to be seated next to a user 108on an airplane. In yet another non-limiting example, a currentlyproximate subject 116 may be standing opposite a user 108 at a privateoutdoor wedding. The system may be interfaced to Transportation SecurityAdministration (TSA) and other agencies worldwide to validate the user'sability to fly based on susceptibility profile and/or degree oftransmission threat as described below in more detail. In yet anothernon-limiting examples, a user could be inviting a dinner guest over,whereas the user would have knowledge of the user “personal healthsusceptibility profile and inoculation status” or other status, as wellthe guest could view the originator susceptibility profile andinoculation status. As defined in this disclosure, an “inoculationstatus” of a person and/or proximate subject includes a status regardingimmunity to a pathogen, including without limitation vaccination status,natural inoculation status as determined by infection history, antibodytiters, or the like, and/or status indicating whether the person inquestion has been invited to opt in to reporting inoculation status, hasopted in to reporting inoculation status, has opted out of reportinginoculation status, and/or has partially opted in, such as for instanceindication of willingness to report some but not all requested datapertaining to inoculation status, for instance and without limitation asdescribed in further detail below. In yet another non-limiting example,the user could determine where they wish to eat in a geographical areabased on asking the system to show the safest (lowest probability ofvirus/pathogen) location as well as food choices the density ofrestaurant clients as well as fluctuations in room destiny based on timeof day. Reservations may be leveraged to find “best times” for lowestrisk exposure. The restaurant may post its sanitization mandate on tabledistance and mask wearing as well as any reported lack compliancereports. In another example, the system could issue an area-wide and/orhyper-localized “contagion” index, similar to a smog alert. This alertsystem may be hyper-localized. A “contagion” index may be furtherrefined based on a user's own susceptibility profile and/or a user's ownrisk of becoming ill and/or developing complications from a pathogen. Inyet another non-limiting example, a currently proximate subject 116 maybe a known relative and/or friend of a user 108. For example, acurrently proximate subject 116 may include a user's grandmother who mayvisit a user 108 in another state for the user's graduation ceremony.Detection of a currently proximate subject location or projectedlocation 116, may include detecting the presence of the currentlyproximate subject 116 within a specified location, detecting thedistance of the currently proximate subject 116 in relation to a user108, another currently proximate subject 116, a landmark, and the like.Detection of a currently proximate subject 116 may include detectingdata including distance relating to the currently proximate subject 116as described below in more detail. Currently proximate subject 116 mayoperate a remote device 120. Remote device 120 may include any processoras described in this disclosure. Remote device 120 may include, beincluded in, and/or communicate with a mobile device, such as a mobiletelephone or smartphone or other IoT device. Remote device 120 mayinclude a single processor operating interpedently, or may include twoor more processors operating in concert, in parallel, sequentially, orthe like; two or more processors may be included together in a singleprocessor or in two or more processors. Remote device 120 may include asupervisory control and data acquisition (SCADA) information system thatmay obtain information and/or data from an environmental sensor. Remotedevice 120 may include but is not limited to, for example, a processoror cluster of processors in a first location and a second processor orcluster of processors in a second location. Remote device 120 mayinclude one or more processors dedicated to data storage, security,distribution of traffic for load balancing, and the like. Remote device120 may distribute one or more computing tasks as described below acrossa plurality of processors of processor, which may operate in parallel,in series or in a mesh or star, redundantly, or in any other manner usedfor distribution of tasks or memory between processors. Remote device120 may be implemented using a “shared nothing” architecture in whichdata is cached at the worker, in an embodiment, this may enablescalability of system 100 and/or processor. A user 108 may be inpossession of wearable device 104, when wearable device 104 is locatedon the user 108, in contact with the user 108, in the possession of anarticle owned by the user 108 such as a briefcase, purse, handbag, andthe like or a fixed and or mounted device and/or when wearable device104 is located adjacent to the user 108, such as when wearable device104 may be located on a surface next to the user such as a counter,desktop, and the like.

With continued reference to FIG. 1, detection component 112 may includea sensor 124. A “sensor,” as used in this disclosure, is a device,module, and/or subsystem configured to detect events and/or changes inan environment, generate, and transmit a signal relating to eventsand/or changes in an environment. A sensor 124 may be configured todetect a physiological parameter of a user 108 and/or a currentlyproximate subject 116. A “physiological parameter,” as used in thisdisclosure, is any phenomenon, sign, characteristic, trait, activity,event, and/or feature that may be sensed from a human subject. This mayinclude but is not limited to any biological process, body condition,physiological state, biochemical response, and/or any combinationthereof. A physiological parameter may include but is not limited toheart rate or heart beats, heart rate variability (HRV), blood volumepulse (BVP) or blood flow, electrocardiogram signals, volumetricvariations of blood volume and/or blood flow, blood oxygen, respiratoryrate or breath, brainwave, skin temperature, skin conductivity, eyemotion, speech rate, facial expression, body posture, speech, foullanguage choices, speed of speech communication, volume, cadence, andthe like. Sensor 124 may be configured to have one or more methodsand/or programming capabilities for calibration and/or validation.

With continued reference to FIG. 1, sensor 124 may include a motionsensor such as an inertial measurement unit, and/or any component thatmay be included in an inertial measurement unit. As used herein, aninertial measurement unit measures and reports a body's specific force,angular rate, and magnetic field surroundings the body, An inertialmeasurement unit may include devices such as a gyroscope and/or anaccelerometer. A motion sensor may include a gyroscope, which may detectorientation changes of a gyroscope; multiple gyroscopes may detectorientation changes with respect to multiple axes, such as threegyroscopes to detect orientation changes with respect to three axes ofrotation, or the like. A motion sensor may include an accelerometer,such as one or more microelectromechanical systems (MEMS) devices. Anaccelerometer may measure acceleration or position in two or more axes;alternatively or additionally, an accelerometer may include a pluralityof accelerometers to detect acceleration with respect to a plurality ofaxes, such as without limitation three accelerometers that detect motionwith regard to three dimensional axes. A motion sensor may include aninertial measurement unit (IMU), which may include multiple types ofmotion sensors in a single chip or system. A motion sensor may bemounted to one or more parts of user's body to detect motion thereof.Changes in patterns in user motion may indicate a transition by userfrom one state of wakefulness or sleep to another; for instance, a steptowards a deeper sleep state or in a direction transitioning from wakingto sleep, as described in further detail below, may be accompanied by adecrease in or cessation of movement by user, and/or by an increasedregularity of chest movements indicating regular breathing in a patternindicative of incipient slumber. A motion sensor may detect changes inpatterns of gait, including any locomotion achieved through movement ofhuman limbs. Changes in patterns of gait may include observingdifferences in limb-movement patterns, overall velocity, forces, kineticand potential energy cycles, changes in contact with a surface such asthe ground or floor and the like.

With continued reference to FIG. 1, a sensor 124 may include a thermalsensor. Thermal sensor may be any sensor that acquires skin temperatureof user's body or a portion thereof. Thermal sensor may include athermometer. Thermometer may be any device that measures temperature.Thermometer may include for example, a mercury thermometer, anelectronic thermometer, or an infrared thermometer. Thermal sensor mayinclude, without limitation one or more infrared sensors, which may becomposed of thermoelectric/pyroelectric materials or semiconductordevices, such as photodiodes or photoconductors, thermistors,thermocouples, or any other elements or components used in digitaland/or electric thermometers or other temperature sensors. Thermalsensor may measure temperature at one or more locations on a user'sbody. For example, thermal sensor may be placed at or in the mouth, inthe ear, in the armpit, and/or in the rectum. Thermal sensor may includenon-contact temperature sensors where temperature may be detected ormeasured remotely, for instance by capturing infrared radiation.

Continuing to refer to FIG. 1, a sensor 124 may include anelectrophysiological sensor. An electrophysiological sensor may be anydevice or component that measures a physiological parameter of a userand generates an electrical signal as a function of the measurement. Aphysiological parameter may include any information that may be sensedfrom user's body, including without limitation any electrical, chemical,optical, auditory, olfactory, kinetic, or other information; aphysiological parameter may include, without limitation, galvanic skinresponse or skin conductance response, pulse rate, breathing rate, bloodflow, heartbeat signatures, electrolyte type and/or concentration, bloodmetabolite levels or ratios, blood pH level, position and/or balance,body strain, neurological functioning, brain activity, brain waves,blood pressure, cranial pressure, hydration level, auscultatoryinformation, skin and/or core body temperature, facial emotions, eyemuscle movement, body movement, blood volume, inhaled and/or exhaledbreath volume, exhaled breath physical and/or chemical composition,reflex response sleepiness, response to external stimuli, swallowingvolume, swallowing rate, head position or tilt, internal body sounds,functional near-infrared spectroscopy signals, snoring, and/or otherphysiological information. Electrophysiological sensor may be utilizedto detect a biological parameter such as heart rate, heart ratevariability, blood volume, pulse, respiratory rate or breathing rate,brainwaves, skin temperature, skin conductivity, eye motion, speechrate, facial expression, and/or body posture. In an embodiment, sensor124 may be programmed using one or more algorithms such as but notlimited to an acoustic and/or kinetic algorithm to measure certain bodyfunctions, including any of the body functions described herein.

Still referring to FIG. 1, an electrophysiological sensor may include,without limitation, a sensor that detects an electrical, magnetic, orelectromagnetic parameter, state, or reading regarding body of user. Anelectrophysiologic sensor may include an electrodynamic sensor deviceconfigured to sense an electrical activity of the heart of a user. Forexample, the electrodynamic sensor may be configured to sense a heartrate or heart rate variability pattern using electrical activity of theheart, for instance using electrocardiography (ECG or EKG), orconductivity. Electrocardiography may include a process of recordingelectrical activity of a heart over a period of time using electrodesplaced on the skin; electrodes may detect tiny electrical changes on theskin that arise from a heart muscle's electrophysiologic pattern ofdepolarizing during each heartbeat. An ECG may be used to measure rateand rhythm of heartbeats or other patterns relating to heartbeats,including without limitation heart rate variability patterns. Electrodesmay be placed in contact with user's skin using any suitable means,including adhesion or incorporation in a wearable device such as a bandof elastic material around user's torso, that places electrodes incontact with user's skin. In some embodiments, direct contact may not benecessary, and electrical functioning may be monitored capacitively,inductively, electromagnetically, or a combination of these approaches.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which EKG data may becollected consistently with the instant disclosure.

With continued reference to FIG. 1, an electrophysiologic sensor mayinclude a sensor that monitors neurological functioning. As anon-limiting example, electrophysiologic sensor may include one or moresensors that perform an electroencephalogram (EEG); EEG may involvedetection of patterns, such as brainwaves, otherwise known as neuraloscillations. EEG may be performed by detection of electrical patternsin neural activity using electrodes contacting user's cranium, such aselectrodes placed along a forehead of user. Electrodes may be adhered touser or incorporated in a wearable device, such as without limitation anearpiece or item of headgear placing electrodes at cranial locationssuch as a forehead or temple. In some embodiments, direct contact maynot be necessary, and neurological functioning can be monitoredcapacitively, inductively, electromagnetically, or a combination ofthese approaches. In some embodiments, brain waves may couple with lowfrequency acoustical sensors integrated into a head-mounted module, orthe like. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which EEG data may becollected consistently with the instant disclosure.

Continuing to view FIG. 1, an electrophysiologic sensor may include asensor configured to perform an electrooculogram (EOG); EOG may bedefined as an electrophysiologic measurement of eye motion. EOG may becollected using electrodes mounted at or near user's eyes, for instancethrough use of a mask or other wearable device that contacts the user'seyelids or rests nearby. EOG may be detected through contactless meanssuch as capacitive, inductive, or electromagnetic detection.Alternatively or additionally, an electrophysiologic sensor may includeelectrodes or other sensors for monitoring an electromyogram (EMG)signal measuring electrical activity of muscles or muscular tissue of auser. An electrophysiologic sensor may include an electrodermal activity(EDA) sensor, also known as skin conductance, galvanic skin response(GSR) sensor, electrodermal response (EDR) sensor, or the like, whichmay measure continuous variation in electrical characteristics of skin.

Still viewing FIG. 1, an electrophysiological sensor may include one ormore sensors configured to detect arterial or vascular data. Forinstance and without limitation, an electrophysiological sensor mayinclude a photoplethysmography (PPG) sensor, which may sense the body'srate of blood flow using a light-based technology whereby a light sourceis emitted through or at tissue containing blood vessels, and lightreflected by or transmitted through the tissue is measured. Anelectrophysiological sensor may include an impedance plethysmograph formeasuring changes in volume within an organ or body (usually resultingfrom fluctuations in the amount of blood or air it contains). Forexample, an impedance plethysmograph to monitor blood pressure inreal-time. An electrophysiological sensor may include a sensor to detectpulse oximetry, where pulse oximetry is a standard noninvasive techniqueof estimating blood gas levels. Pulse oximeters typically employ two ormore optical wavelengths to estimate the ratio of oxygenated todeoxygenated blood. Similarly, various types of hemoglobin, such asmethemoglobin and carboxyhemoglobin may be differentiated by measuringand comparing the optical absorption at key red and near-infraredwavelengths. Additional wavelengths may be incorporated and/or replaceconventional wavelengths. For example, by adding additional visible andinfrared wavelengths, myoglobin, methemoglobin, carboxyhemoglobin,bilirubin, SpCO.sub.2, and blood urea nitrogen (BUN) may be estimatedand/or monitored in real-time in addition to the conventional pulseoximetry.

With continued reference to FIG. 1, an electrophysiological sensor maymonitor blood pressure, using, as a non-limiting example, a digitalblood pressure monitor; digital blood pressure monitor may includeactuators and sonic and pressure transducers placed on the skin, and maymeasure systolic and/or diastolic pressure, for instance by monitoring apressure at which a “Korotkoff sound” is first heard (systolic), thendisappears (diastolic). This technique may also be used to monitorintra-cranial pressure and other internal pressures. Blood pressure mayalso be measured by comparing the time between pulses at differentregions of the body. An electrophysiological sensor may alternatively oradditionally include pyroelectric sensor for monitoring heart rate,heart rate variability patterns, and the like.

With continued reference to FIG. 1, a sensor 124 may include anelectrodermal sensor such as a galvanic skin response (GSR),electrodermal response (EDR), psychogalvanic reflex (PGR), skinconductance response (SCR), sympathetic skin response (SSR) and/or skinconductance level (SCL). As used herein, an electrodermal sensormeasures continuous variations in electrical characteristics of theskin. This may include but is not limited to measurement of sweatingresponse as controlled by a user's sympathetic nervous system. In anembodiment, changes in sweat response and blood flow response may bemeasured and recorded as changes in electrical conductance between twopoints over time. Galvanic skin response (GSR) may include placing twoelectrodes on a user and applying a weak current, resistance andelectrodermal activity may then be recorded. In an embodiment, a painfulstimulus may elicit a sympathetic response by sweat glands such as anincrease in secretion of sweat containing water and electrolytes. Thisincrease in sweat may then lower electrical resistance of the skin andaffect GSR readings. Electrodermal dermal sensor may also detectvasodilation of blood vessels, such as for example during exertion whenincrease sweating occurs and a user's face may turn red.

Continuing to refer to FIG. 1, a sensor 124 may include an acousticsensor, such as a microphone or the like. An acoustic sensor may detectand/or monitor breathing characteristics of user, for instance viaauscultatory signal extraction. In an embodiment, an acoustic sensor maybe used to sense sounds associated with breathing, coughing, andvariations in coughing such as wet or dry coughs, frequency of coughs,key words, stuttering vomiting, urination, toilet flushing, runningwater (washing hands), snoring, labored breath, changes in speechcadence and or volume. In an embodiment, an acoustic sensor may measureambient environmental sounds such as a train crossing a train track orthe sound of water running over rocks in a stream. The microphone mayalso be used with DSP to determine if the user or users in the proximatearea are wearing a surgical face mask, a N95 face mask, a KN95 facemask, a respirator, an airline, an oxygen mask, a face shield, or thelike. Signal processing algorithms may then be used to extract breathingsounds from other sounds and noise, for instance using digital signalfiltering or noise elimination processes. This information may be used,as a non-limiting example, to measure and/or track intensity, volume,and speed of breathing, which may in turn be used to determine a user'sstate of exertion, and exercise tolerance. Alternatively oradditionally, an acoustic sensor may monitor breathing using employpressure transducers. For instance, and without limitation, changes inpressure inside or near the ear associated with breathing may bemeasured directly and, through signal processing, translated into abreathing monitor. Similarly, optical reflection sensors may be used tomonitor pressure by monitoring physical changes in the skin or tissuesin response to breathing. For monitoring the physical changes of thetympanic membrane in response to breathing, and hence ascertainingbreathing rate, an optical signal extraction approach may be employed.Microphones can also hear coughing, shortness of breath, troubledbreathing indicative of infection such as wheezing, sputtering, shallowrespirations, rhonchi, stridor, crackles, bradypneas, apnea, Kussmaul'srespirations, Cheyen-Stokes, apneustic breathing, hyperventilation,agonal respirations, and the like.

With continued reference to FIG. 1, a sensor 124 may include a chemicalsensor, defined as a sensor that detects airborne chemicals andbyproducts, including gases, aerosols, and/or particulate matter.Chemical sensor may be used to detect, without limitation, chemicalsconsistent with human presence, such as CO₂, H₂, methane, oxygen, andthe like. Chemical sensor may be used to detect one or more volatileorganic compounds (VOCs) that may be present in the environment andsurrounding locations of a user 108 and/or currently proximate subject116, including but not limited to carbon dioxide, hydrogen gas,flammable volatiles, and the like. Chemical sensor may be used to detectone or more chemicals that may be disease producing when exposed to ahuman subject, including but not limited to asbestos, radon, cadmium,benzene, carbon monoxide, soot, lead, mercury, uranium, chlorinatedhydrocarbon solvents, carbon disulfide, nitrates, methylene chloride,methyl mercury, cyanide, pesticides, polychlorinated biphenyls (PCBs),carbon tetrachloride, vinyl chloride and the like.

With continued reference to FIG. 1, sensor 124 may include a biosensor,defined as a sensor configured to detect aerosolized bacteria, viruses,and/or fungal spores. A biosensor may detect aerodynamic particle size,particle fluorescence, respirable aerosols, parties, and the like. Abiosensor may include an analytical device containing a sensingbioreceptor configured to using optic, electrochemical, and/orpiezoelectric technology to detect distinctive analytes such asproteins, and/or nucleic acids indicative of various bacteria, viruses,toxins, fungi, pollen, and the like that may be present in theenvironment, on a user 108, and/or on a currently proximate subject 116.For instance and without limitation, a biosensor may be configured todetect Human Immunodeficiency Virus (HIV), hepatitis, Ebola, Norovirus,Influenza, Dengue fever, Coronavirus, Epstein Barr virus, HumanPapilloma Virus (HPV), Rhinovirus, Japanese encephalitis virus, and thelike.

With continued reference to FIG. 1, detection component 112 may includea camera 128. A “camera,” as used in this disclosure, is a device thatcaptures pictures, videos, or other visual images. Camera 128 mayinclude for example, an electronic device containing a lens that is usedto take pictures. Camera 128 may detect body movement of a user, whichmay be used similarly to body movements detected by sensor 124; camera128 may, for instance, capture a sequence of images of user's body andcompare images of the sequence of images to determine whether user hasmoved user's body, and if so, how frequently or to what extent. Camera128 may detect facial expressions of a user, which may determine whetheruser is over-exerted or unphased by some sort of physical activity forexample. Camera 128 may detect a mask on the user or on the individualsin the area. Camera 128 may be configured to detect density of alocation, such as the density of a sports stadium where a user 108 maybe located. This may include determining and/or examining the populationdensity of a crowd in an entire location such as the entire sportsstadium, and/or the population density of where the user 108 is exactlylocated within the entire sports stadium. Camera 128 may prompt a user108 to position camera 128 to point to a certain area or take aphotograph of four currently proximate subjects 116 that may be locatednearby.

With continued reference to FIG. 1, camera 128 may include an opticalcamera. An “optical camera,” as used in this disclosure, is a devicethat generates still and/or video images by capturing senseselectromagnetic radiation in the visible spectrum, having wavelengthsbetween approximately 380 nm and 740 nm; radiation in this range may bereferred to for the purposes of this disclosure as “visible light.”Optical camera may include a plurality of optical detectors, where an“optical detector” is defined as an electronic device that device thatalters any parameter of an electronic circuit when contacted by visiblelight. Optical detectors may include, without limitation, charge-coupleddevices (CCD), photodiodes, avalanche photodiodes (APDs), siliconphoto-multipliers (SiPMs), micro-channel plates (MCPs), micro-channelplate photomultiplier tubes (MCP-PMTs), photoresistors, and/orphotosensitive or photon-detecting circuit elements, semiconductorsand/or transducers. APDs, as used herein, are diodes (e.g. withoutlimitation p-n, p-i-n, and others) reverse biased such that a singlephoton generated carrier can trigger a short, temporary “avalanche” ofphotocurrent on the order of milliamps or more caused by electrons beingaccelerated through a high field region of the diode and impact ionizingcovalent bonds in the bulk material, these in turn triggering greaterimpact ionization of electron-hole pairs. APDs may provide a built-instage of gain through avalanche multiplication. When a reverse bias isless than breakdown voltage, a gain of an APD may be approximatelylinear. For silicon APDs this gain may be on the order of 10-100.Material of APD may contribute to gains.

Still referring to FIG. 1, individual photodetectors in optical cameramay be sensitive to specific wavelengths of light, for instance by useof optical filters to exclude such wavelengths; for instance, andwithout limitation, some photodetectors may be sensitive to blue light,defined as light having a wavelength of approximately 420 nm to 480 nm,some may be sensitive to green light, defined as light having awavelength of approximately 534 nm to 545 nm, and some may be sensitiveto red light, defined as light having a wavelength of approximately 564nm to 580 nm. Combinations of photodetectors specifically sensitive tored, green, and blue wavelengths may correspond to wavelengthsensitivity of human retinal cone cells, which detect light in similarfrequency ranges. Photodetectors may be grouped into a three-dimensionalarray of pixels, each pixel including a red photodetector, a bluephotodetector, and a green photodetector. Pixels may be small enough tofit millions into a rectangular array less than an inch across. Opticalcamera may include one or more reflective and/or refractive componentsthat focus incident light onto photodetectors.

With continued reference to FIG. 1, camera 128 may include an infraredcamera. An “infrared camera,” as used in this disclosure, is a camerathat detects electromagnetic radiation in the infrared spectrum, definedas a spectrum of radiation having wavelengths between approximately 750nm and 1 mm. As a non-limiting example, infrared camera may detect lightin the 0.75 to 1.5 μm range. Infrared camera may include a plurality ofinfrared detectors, where an “infrared detector” is defined as anelectronic device that device that alters any parameter of an electroniccircuit when contacted by infrared light. Infrared detectors mayinclude, without limitation, silicon photodiodes doped to detectinfrared light and/or microbolometers. A “microbolometer” is defined forthe purposes of this disclosure as a specific type of bolometer used asa detector in a thermal camera. Microbolometer may detect infrared lightwhen infrared radiation with wavelengths between 7.5-14 μm strikesdetector material, heating it, and thus changing its electricalresistance.

Continuing to refer to FIG. 1, infrared camera may use a separateaperture and/or focal plane from optical camera, and/or may beintegrated together with optical camera. There may be a plurality ofoptical cameras and/or a plurality of infrared cameras, for instancewith different angles of perspective on a subject area. Alternatively oradditionally, two or more apparatuses coordinated using a mesh network,as described in further detail below, may be combined to generate two ormore images from varying perspectives to aid in three-dimensionalimaging and/or analysis.

With continued reference to FIG. 1, detection component 112 may beconfigured to perform simultaneous localization and mapping (SLAM). Thismay include constructing and/or updating a map of an unknown environmentsuch as a location and tracking a user 108 and/or a currently proximatesubject 116 within the location. This may include calculating one ormore SLAM algorithms to determine approximations and/or locationmapping, including but not limited to particle filter, extended Kalmanfilter, covariance intersection, and/or GraphSLAM. SLAM may includedetermining a topology of a room and/or location, mapping an entirelocation such as every room in a house, obtaining coordinates,evaluating ceiling height and furniture placement within a location, togenerate a 3 Dimensional map of a location. Employment of SLAM mayinclude but is not limited to employment of lidar technology. Lidartechnology may include measuring distances within a location bymeasuring distances and/or ranges by illuminating a target with a laserlight and measuring the reflection with a detection component 112 suchas a sensor 124 and/or a camera 128. This may include imaging, acoustic,radar, lidar, time-of-flight, and/or other sensors 124 and/or cameras128 configured to detect and map a location, including human subjectsand/or animals that may be present in a location. In an embodiment, thismay include detecting and/or imaging using radio frequencies includingterahertz or millimeter wave imagers, seismic sensors, magnetic sensors,weight/mass sensors, ionizing radiation sensors, and/or acousticalsensors, to accurately recognize and spatially determine a locationand/or human subjects and/or animals. This may include, but is notlimited to stereo imaging, photogrammetry, time of flight methods,structured light, shape from motion, shape from polarimetry, lidar,radar, sonar, synthetic aperture imaging, multiple disparate apertures,gated imaging, single or multiple pixel range finding, artificialintelligence methods, and/or other methods.

With continued reference to FIG. 1, information captured relating to acurrently proximate subject 116 may be collected under certainconditions where the currently proximate subject 116 may be forced toconsent to have data captured and/or collected about themselves. Forinstance, and without limitation, this may occur with an email plug in,such as when a currently proximate subject 116 may be known to bedangerous and/or a public health threat. In yet another non-limitingexample, this may occur when a government agency or municipality mayrequire greater transparency relating to personal information, such asfor example during natural hazards such as an earthquake, a tsunami, apandemic, an act of terrorism, and the like. In an embodiment,information may be captured about a user 108 and/or a currentlyproximate subject 116 based on various situations where the user 108and/or currently proximate subject 116 may consent because of a benefitand/or reward. In an embodiment, a user 108 may consent to informationbeing shared for a situation such as when at the airport, while the user108 may not consent to information and/or select information beingshared when the user is at a public establishment. For instance andwithout limitation, the TSA may obtain information such as a degree oftransmission threat about a user 108 and/or a currently proximatesubject 116 as a way to fast track an approval for the user 108 and/orthe currently proximate subject 116 to board a flight. Such informationmay also be utilized to determine the best location for a user 108and/or a currently proximate subject 116 to sit on an airplane, onanother mode of public transportation such as a bus or train, and/or ata stadium for a sporting event, concert, or any other various forms ofentertainment, based in part on the individual's personal healthsusceptibility profile. In an embodiment, governmental agencies may useinformation pertaining to possible degrees of threat calculated bywearable device 104 to create rules, laws, enforce rules and/or laws,create context specific procedures, allow flexibility, and create clearand/or predictable results. In an embodiment, governmental agenciesand/or concerned parties may be able to track information and/or trendsobtained by wearable device 104, but may have limited and/or no accessto any personal health information (PHI), and/or identifying informationabout a user 108 and/or a currently proximate subject 116 that may bestored and/or contained within wearable device 104 and/or an externaldatabase. For instance and without limitation, during a pandemic, agovernmental agency such as the Department of Homeland Security mayaccess data stored, calculated, and/or contained within wearable device104 to evaluate trends and/or outbreaks within certain geographicallocations, among certain demographic populations, within certaincommunities and the like. In such an instance, such information accessedby a governmental agency may not contain any identifiable information,and/or information that may breach security laws such as but not limitedto the Health Insurance Portability and Accountability Act (HIPAA).

With continued reference to FIG. 1, camera 128 may be configured todetect a user 108 and/or a currently proximate subject 116 wearingpersonal protective equipment (PPE) such as but not limited torespiratory protection such as a surgical face mask, a N95 face mask, aKN95 face mask, a respirator, an airline, an oxygen mask, a face shieldand the like, eye protection such as spectacles, goggles, shields,visors, and the like, hearing protection such as ear muffs, ear plugs,and the like, hand protection such as gloves, mittens, insulated gloves,wire mesh gloves, latex, vinyl, and/or nitrile gloves, chemicalresistant gloves, and the like, foot protection such as shoes, boots,shoe covers and the like, head protection such as helmets, caps, hoods,hats, and the like, skin protection such as scrubs, surgical gowns, andthe like. This may be useful, such as if a hospital needs to monitornon-professional staff, and/or visitors to deal with challengespresented in nosocomial settings. In an embodiment, PPE such as a maskmay be detected based on information such as telephone use, facialrecognition, speaker recognition including with or without wearing amask and the like. Camera 128 may be configured to detect and/or computea compliance value of a user 108 and/or a currently proximate subject116 who may be wearing an item of PPE. A “compliance value,” as used inthis disclosure, is a score indicating how well a user 108 and/or acurrently proximate subject 116 may follow recommended guidelines,instructions and/or usage of equipment. For instance and withoutlimitation, a compliance value may specify that a user 108 is wearing apiece of PPE such as a facemask, but the user is not wearing thefacemask correctly, and as such the compliance value may indicate a lowcompliance with a recommendation to wear a facemask. Informationpertaining to a compliance value may be stored within user database, asdescribed below in more detail. A compliance value may contain anumerical, character, and/or reference value score associated with auser's 108 and/or currently proximate subject's 116 compliance with arecommendation. For instance and without limitation, a compliance valuemay be scored on a numerical continuum, where a score of 0 may indicatea user who has very little if any compliance with a recommendationand/or guideline, while a score of 50 may indicate a user who hasmoderate compliance, and a score of 100 may indicate a user who has veryhigh compliance. In an embodiment, camera 128 may detect a compliancevalue of a user 108 and/or a currently proximate subject 116 who may belocated adjacent to the user 108 for example, as means to detect andascertain if the currently proximate subject 116 is wearing a mask. Inan embodiment, sensor 124 may be utilized to detect and perform a fastFourier transformation (FFT) analysis on audio that may be captured,such as a phone call to determine if a user 108 and/or a currentlyproximate subject 116 is wearing a mask and if that can be determinedbased on audio that may be detected. In an embodiment, a mask may beproduced to contain an RFID, NFC, and/or BTLE tag that may informwearable device 104 if user 108 and/or currently proximate subject 116is wearing a mask. In an embodiment, PPE equipment may contain amicrophone that may detect a sonic signature that may identify useand/or compliance with PPE equipment.

With continued reference to FIG. 1, detection component 112 may beconfigured to detect a baseline location measurement. A “baselinelocation measurement,” as used in this disclosure, is an initialassessment of a location, to be utilized for future comparisons. A“location,” as used in this disclosure, is a place and/or position wherea user 108 and/or a currently proximate subject 116 may be located. Alocation may include a public and/or private permanent or transientsite. For example, a location may a specific room at a private residencewhere a party may be hosted. In yet another non-limiting example, alocation may include a transient site, such as a ferry that travels backand forth between an island and mainland. Wearable device 104 mayinclude location component 132. A “location component,” as used in thisdisclosure, is a component that aids in the identification of alocation. In one embodiment, the location component exploits receivedsignal strength indicator (RSSI) and other signal strength methodologiesto determine the proximity of the proximate individual or swarm. RSSImay include an estimated measure of power level and/or signal thatwearable device 104 may receive and/or detect from a device belonging toa currently proximate subject 116, such as remote device 120. At alarger distance, a signal may get weaker and wireless data may getweaker, leading to a lower overall data throughput. A signal may bemeasured by RSSI which may indicate how well wearable device 104 canhear and/or detect a currently proximate subject 116. In an embodiment,RSSI may be measured with numerical outputs, where the greater the RSSIvalue, the stronger the signal. For example, an RSSI value representedin a negative numerical value such as −53, the closer the RSSI value isto 0, the stronger the received signal has been. In an embodiment,signal strength may be detected and/or measured based on factors such asphysical obstructions, competing Wi-Fi networks, other electronicdevices, and the like. This may include the use of detecting Bluetoothsignals and/or signal strength, 3G, 4G, and 5G technology, micro cells,and/or macro cells. A location component 132 may include a globalpositioning system (GPS) which may identify the exact whereabouts of alocation, using signals from a satellite. A GPS may identify the addressof where a user 108 and/or a currently proximate subject 116 may belocated. A GPS may identify the longitudinal and latitudinal coordinatesof where a user 108 and/or a currently proximate subject 116 may belocated. A GPS may identify a geographical area where a user 108 and/ora currently proximate subject 116 may be located. For instance, andwithout limitation, a GPS may identify that a user 108 is located in thePacific Northwest region of the United States. A location component 132may identify a location using an internet protocol (IP) address, thatmay identify and extrapolate where a user 108 and/or a currentlyproximate subject 116 may be located. A location component 132 mayidentify a location using an internet service provider (ISP) and acorresponding traceroute to identify where a user 108 and/or a currentlyproximate subject 116 may be located. A location component 132 mayidentify a location using an International Air Transport Association(IATA) airport code that may be located nearby and/or in proximity to auser 108 and/or currently proximate subject 116. For instance andwithout limitation, location component 132 may identify a user 108 asbeing located near IATA code “ORD” which may indicate that the user 108is located somewhere near Chicago, and thus located in the Midwestregion of the United States. In an embodiment, location component 132may receive an input from user 108 and/or currently proximate subject116 specifying a location where user 108 and/or currently proximatesubject 116 may be located. One or more frequent locations where a userspends a lot of time or repeatedly visits may be saved and stored withinuser database 136. For instance and without limitation, the address of auser's office building where a user 108 works each day may be stored andsaved as “office,” while the address of a coffee shop where a user 108stops and buys coffee from each morning may be saved as “coffee.” In anembodiment, information contained within user database 136 may includeinformation describing a user's overall mental and emotional well-beingincluding for example if the user feels anxious or lonely. For example,a user 108 and/or a currently proximate subject 116 may report feelingdepressed about a recent national disaster. Information contained withinuser database 136 may also describe where a user 108 and/or a currentlyproximate subject 116 may have traveled to, what mode of transportationwas utilized, and/or how long a user visited a particular location andwhere the user visited within the location and what accommodations wereemployed.

With continued reference to FIG. 1, information relating to a baselinelocation measurement may be stored within user database 136. Userdatabase 136 may be implemented, without limitation, as a relationalauthorization database, a key-value retrieval authorization databasesuch as a NOSQL authorization database, and/or any other format orstructure for use as a user database 136 that a person skilled in theart would recognize as suitable upon review of the entirety of thisdisclosure. User database 136 may alternatively or additionally beimplemented using a distributed data storage protocol and/or datastructure, such as a distributed hash table or the like. Data entries inuser database 136 may be flagged with or linked to one or moreadditional elements of information, which may be reflected in data entrycells and/or in linked tables such as tables related by one or moreindices in a relational user database 136. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousways in which data entries in a user database 136 may be stored,retrieved, organized, and/or be utilized to reflect data and/or recordsas used herein, as well as categories and/or populations of dataconsistently within this disclosure.

With continued reference to FIG. 1, detection component 112 isconfigured to detect a currently proximate subject 116 in relation to alandmark. A “landmark,” as used in this disclosure, is a natural and/orartificial feature contained within a location. A natural landmark mayinclude a landmark that is created by and/or relating to nature. Forexample, a natural landmark may include a tree or stone that may bepresent in a public location such as a park. In yet another non-limitingexample, a natural landmark may include a live plant that may be locatedatop a table at a restaurant. An artificial landmark may include alandmark that is made and/or produced by mankind and/or machinery. Forexample, an artificial landmark may include an item of furniture in alecture hall of a university, such as a desk located in the middlesection of the classroom, three rows back from the center stage. Alandmark may include a structure such as a school building that may belocated in a town. A landmark may include an anatomical structure thatmay be used as a point of orientation within a location. For example,landmark may include a crack in a ceiling that is uniquely found in alocation such as a user's office. A landmark may include an object thatmay be used as a point of orientation within a location. For example, alandmark may include an object such as a photograph of a replication ofthe Mona Lisa, which may be located in the waiting room of a dentaloffice. In an embodiment, a landmark may include a temporal componentand/or timestamp that may indicate when a landmark was detected, toaccount for residency placement and/or duration of a landmark. Detectioncomponent 112 may detect and store information relating to a currentlyproximate subject 116 in relation to a landmark, within currentlyproximate subject database 140. Currently proximate subject database maybe implemented as any data structure suitable for use as user database136, as described above in more detail. In an embodiment, detectioncomponent 112 may detect the location of currently proximate subject 116in relation to landmark and/or user 108. For instance, and withoutlimitation, detection component 112 may detect that a user 108 islocated six feet away from landmark, while currently proximate subject116 is located eight feet away from landmark, and two feet away fromuser 108. Information relating to location of currently proximatesubject 116 in relation to landmark and/or user 108 may be utilized toprovide information to a user regarding mitigation components andprotected locations, as described below in more detail.

With continued reference to FIG. 1, detection component 112 isconfigured to detect a first currently proximate subject 116 in relationto a second currently proximate subject 116. Detection component 112 maydetect the location of a first currently proximate subject 116 within alocation, in relation to a second currently proximate subject 116 withinthe location. Information relating to location of first currentlyproximate subject 116 and/or second currently proximate subject 116 maybe stored and contained within currently proximate subject database 140.For instance and without limitation, detection component 112 may detectthat a first currently proximate subject 116 such as a guest at awedding reception, is located three feet away from a second currentlyproximate subject 116 such as the bride, at an outdoor wedding tentlocated in Bangor, Me. In yet another non-limiting example, detectioncomponent 112 may detect that a first currently proximate subject 116such as a first child located in a classroom in Dallas, Tex., is sittingat a desk located eight feet away from a second currently proximatesubject 116 such as a second child located in the same classroom.Detection component 112 may be configured to detect the location of auser 108 in relation to a first currently proximate subject 116, asecond currently proximate subject 116, and/or a plurality of currentlyproximate subjects 116, and quantify risk of currently proximatesubjects 116 based on specified distances that may indicate increasedrisk and/or harm to user 108. Detection component 112 may be configuredto triage and detect currently proximate subjects 116 that may indicatethe greatest risk of harm and/or contagion for a user. For instance, andwithout limitation, detection component 112 may detect a location thatcontains a plurality of currently proximate subjects 116, such as abirthday party that has fifty guests crowded into a living room. In suchan instance, detection component 112 may detect that forty of the fiftyguests do not pose any real harm to the user 108, because all forty ofthe guests have left the living room and are now located in thebackyard. However, of the ten remaining guests in the living room, threemay be of high risk in relation to the user 108, because they arelocated within three feet of the user, while seven may be of low risk,because they are located twelve feet away from the user 108. In anembodiment, detection component 112 may evaluate the distance betweenthe three-party guests to one another and also evaluate the distancebetween the three-party guests and the user 108. In yet anothernon-limiting example, detection component 112 may be configured todetect a baseline location measurement of a baseball stadium, which maycontain several thousand fans. In such an instance, detection component112 may stratify risk, but first detecting a currently proximate subject116 who may be seated next to a user 108, as compared to anothercurrently proximate subject 116 who may be located 500 yards away, onanother side of the baseball stadium.

With continued reference to FIG. 1, wearable device 104 may be utilizedto determine trends of pathogens and/or outbreaks of various diseasesthat may be occurring to a group of one or more users 108 and/orcurrently proximate subjects 116 and may aid in creating contextawareness solutions. For instance, and without limitation, a group mayconsist of a classroom of kids at a school and the teacher of theclassroom. Wearable device 104 may be utilized to determine and assesspathogens and/or diseases that the group may be at risk for. In such aninstance, wearable device 104 may evaluate what occurs when a groupmember leaves the environment of the group, and potentially introduces apathogen and/or risk of exposing the pathogen. For example, a parent maywish to send a child to school during an outbreak of a pathogen such asinfluenza, whereby the parent may be concerned that the child may beasymptomatic and may be exposed to influenza and carry the disease backhome, thereby infecting the parent and other family members, neighbors,and friends, as well as any other students, teachers, and/or animalsthat the child may come into contact with. In such an instance, wearabledevice 104 may be employed by the school so that each student andteacher may be engaged in using wearable device 104 when in school andat home. Information regarding secondary locations visited by children,teachers, workers, cleaning crew, and the like may be stored andutilized by wearable device 104 to provide information regarding anoutbreak, and as a means to trace back to the site of the outbreak. Inan embodiment, a parent may be notified about an outbreak and the parentmay decide if the parent wishes to send their child to school. In anembodiment, a child may only be a carrier of a pathogen and/or disease,and a parent may make a decision about risk and overall transmissionrates. In yet another non-limiting example, wearable device 104 may aidin creating contextual awareness solutions regard swarms of individualsin highly dense populations and/or situations that have large numbers ofindividuals in attendance, including sporting events, concerts,protests, rallies, parades, celebrations, holiday gatherings and thelike. For instance and without limitation, wearable device 104 may beused to make informed decisions for a user such as if there may be apossibility of a user contracting a communicable disease such asinfluenza if the user attends a Fleetwood Mac concert at an indoorconcert hall. In yet another non-limiting example, wearable device 104may be configured to assess how long a group of people have been locatedin close proximity and may alert a user when certain time criteria havebeen met. In an embodiment, context awareness solutions may containtimestamps and/or a temporal attribute, describing the duration of adegree of threat. In an embodiment, a degree of transmission threat maybe calculated before a user attends the concert, using informationavailable concerning the event, seating arrangements at the event,prevalence of a communicable disease circulating at the event, air flowand ventilation at the event, and the like.

With continued reference to FIG. 1, wearable device 104 may aid increating a safety profile for intimacy. For example, a wearable device104 may alert a user 108 about the safety and/or wellness of a currentlyproximate subject 116 and the risk of acquiring a sexually transmittedinfection from a potential partner. For instance and without limitation,wearable device 104 may alert a user 108 who may be located at a bar andaround a great number of potential suitors, about the safety and/orrisks that may come with engaging in a sexual relationship with apotential suitor. This may allow a user 108 to make an informed decisionabout if a potential partner is being truthful, what types of sexuallytransmitted infections the user 108 may be at risk of getting, and/orpotential sexually transmitted infections a potential partner may beimmune to.

With continued reference to FIG. 1, wearable device 104 may empower auser 108 to make an informed decision regarding all aspects of dating,courtship, and/or relationships. For example, wearable device 104 may beutilized to evaluate a degree of transmission threat if a user 108 wereto engage in an act such as holding hands with a currently proximatesubject 116 versus the degree of transmission threat if a user 108 wereto kiss the currently proximate subject 116. In yet another non-limitingexample, wearable device 104 may be utilized to determine the risk ofmore intimate situations, such as engaging in sexual intercourse with acurrently proximate subject 116 as opposed to merely holding hands withthe currently proximate subject.

With continued reference to FIG. 1, wearable device 104 includes a datainput component 144. A “data input component,” as used in thisdisclosure, a component that captures data relating to a currentlyproximate subject 116. Data may include information captured usingdetection component 112, including for example sensor 124 and/or camera128 as described above in more detail. Data input component may beconfigured to accommodate various privacy settings, including but notlimited to general data protection regulation (GDPR), and/or Singaporedata collection. In an embodiment, data obtained from a currentlyproximate subject 116 may be obtained without the permission, knowledge,and/or consent of the currently proximate subject 116. For example,sensor 124 may be configured to detect and record the temperature of thecurrently proximate subject 116, without a currently proximate subject116 being aware of this happening. In an embodiment, informationrelating to a currently proximate subject 116 may be stored and savedsuch as in memory, within currently proximate subject database 140. Inyet another non-limiting example, information relating to a currentlyproximate subject 116 may not be stored and may not be saved in memory.In an embodiment, data obtained from a currently proximate subject 116may be obtained with the permission, knowledge, and/or consent of acurrently proximate subject 116. For instance and without limitation,wearable device 104 may transmit to remote device 120, a signalcontaining an input seeking permission from a currently proximatesubject 116 before wearable device 104 measures and/or records the gaitand/or movement of the currently proximate subject 116.

With continued reference to FIG. 1, data may include a physiologicalmeasurement of currently proximate subject 116. A “physiologicalmeasurement,” as used in this disclosure is a measurement of aphysiological parameter, including any phenomenon, sign, characteristic,trait, activity, event, and/or feature that may be sensed from a humansubject. For instance, and without limitation, a physiologicalmeasurement may include a measurement of a currently proximate subject's116 body temperature. In yet another non-limiting example, aphysiological measurement may include a recording of a currentlyproximate subject's 116 breathing patterns to identify anyirregularities and/or indicators of illness such as crackles or shallowbreathing. In yet another non-limiting example, a physiologicalmeasurement may include a photograph of an exposed body part, such as acurrently proximate subject's 116 face that may be analyzed for a markerof disease, illness, and/or future likelihood of illness. For instance,and without limitation, a currently proximate subject 116 that appearsto have flushed skin particularly on the face and who has rapidperspirations may be at risk of a future likelihood of developinginfluenza. In yet another non-limiting example, a currently proximatesubject 116 that appears to have red, swollen, and itchy eyes may be atrisk of developing a coronavirus infection. Data may include informationobtained using any sensor 124 as described above in more detail. Forexample, data may include a measurement using chemical sensor, relatingto any by-products that may expelled by currently proximate subject 116,such as for example, any exhaled permanent gases, any volatile organiccompounds, any exhaled breath condensate, any proteins, lips, and/oroxidation products, any heavy metals and the like. For example, data mayinclude a measurement of an exhaled breath analysis that may indicatehigh levels of cysleukotrienes, specifically LTE4, LTC4, and LTD4. Datarelating to a currently proximate subject 116 may include a distancemeasurement between a currently proximate subject 116 and a user 108 inpossession of wearable device 104. A distance measurement may specifythe amount of space between a user 108 and a currently proximate subject116. For instance, and without limitation, a distance measurement mayspecify that a user 108 and a currently proximate subject 116 arelocated twenty-five feet apart. Distance may be measured using one ormore systems of measurement, including but not limited to United Statescustomary units, metric units, imperial units, international system ofunits, English units, MKS system of units and the like. In anembodiment, a user 108 may preset and select a preferred system ofmeasurement for wearable device 104 to utilize.

With continued reference to FIG. 1, data input component 144 isconfigured to detect a signal from a device in the possession of acurrently proximate subject 116. A “signal,” as used in this disclosure,is a function that conveys information. A signal may include a voltage,current, and/or electromagnetic wave, magnetic induction, opticalsignal, or the like that contains information. In an embodiment, asignal may be received from remote device 120, in possession ofcurrently proximate subject 116, by data input component 144. Data inputcomponent 144 identifies an authenticator of a currently proximatesubject 116 as a function of the signal. A signal may also contain adistance range a currently proximate subject is from a user. An“authenticator,” as used in this disclosure, is participant score of acurrently proximate subject 116. A participant score may indicate howmuch data and/or what types of data may be collected, relating to acurrently proximate subject 116. For example, a participant score mayindicate that a currently proximate subject 116 agrees to shareinformation with wearable device 104 pertaining to the currentlyproximate subject's 116 physiological measurements but that thecurrently proximate subject 116 does not agree to share any personalinformation with wearable device 104, including any demographic data,personal medical data and the like. In yet another non-limiting example,a participant score may indicate that the currently proximate subject116 does not agree to share any information with wearable device 104,and as such wearable device 104 may only capture data that may bepublicly available. In yet another non-limiting example, a participantscore may indicate that the currently proximate subject 116 agrees toshare all information with wearable device 104. In yet anothernon-limiting example, a participant score may indicate that currentlyproximate subject 116 agrees to share user's susceptibility profile withwearable device 104. Wearable device 104 captures data as a function ofan authenticator of a currently proximate subject 116. In an embodiment,a participant score may be used to assess relative risk. For example,someone may want more distance from someone who does not participate insharing data, because there may be less information known about thatperson.

With continued reference to FIG. 1, an authenticator may include anindication as to a user's 108 and/or a currently proximate subject's 116opt-in participation level, with wearable device 104, software run onwearable device 104, data sharing with wearable device 104, dataanalytics performed with wearable device 104, and the like. In anembodiment, there may be various levels of opt-in participation levelsincluding for example, individuals who have never been exposed to apathogen of possible contagion 156 before, individuals who have chosennot to opt in, and/or individuals with known statuses, based oncalculated degrees of threat as described below in more detail. A knownstatus may include but is not limited to a situation where a user 108may have been vaccinated, may not be vaccinated, may have confirmedtiter indicating immunity to a pathogen based on a vaccination and/orexposure, may not participate, may only participate in selectsituations, and the like. In an embodiment, a user 108 may initiallychoose to opt-in, and/or opt-in with certain information such as onlyinoculation records, whereby later on a user 108 may choose to opt-outand/or not share certain information anymore.

With continued reference to FIG. 1, wearable device 104 includes aprocessor 148. Processor 148 may include any processor 148 as describedin this disclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Processor 148 may include, beincluded in, and/or connect with a mobile device such as a mobiletelephone or smartphone. Processor 148 may include a single processor148 operating independently or may include two or more processor 148operating in concert, in parallel, sequentially or the like; two or moreprocessors may be included together in a single processor 148 or in twoor more processors. Processor 148 may interface or connect with one ormore additional devices as described below in further detail via anetwork interface device. Processor 148 may be located remotely fromwearable device 104. Network interface device may be utilized forconnecting processor 148 to one or more of a variety of networks, andone or more devices. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an association, a building, acampus or other relatively small geographic space), a telephone network,a data network associated with a telephone/voice provider (e.g., amobile communications provider data and/or voice network), a directconnection between two processors, and any combinations thereof. Anetwork may employ a wired and/or a wireless mode of communication. Ingeneral, any network topology may be used. Information (e.g., data,software etc.) may be transmitted to and/or from a computer and/or aprocessor 148. Processor 148 may include but is not limited to, forexample, a processor 148 or cluster of processors in a first positionand a second processor 148 or cluster of processors in a secondposition. Processor 148 may include one or more processors dedicated todata storage, security, dispersal of traffic for load balancing, and thelike. Processor 148 may distribute one or more computing tasks asdescribed below across a plurality of processors of processor 148, whichmay operate in parallel, in series, redundantly, or in any other mannerused for dispersal of tasks or memory between processors. Processor 148may be implemented using a “shared nothing” architecture in which datais cached at the operative, in an embodiment, this may enablescalability of system 100 and/or processor 148.

Continuing to refer to FIG. 1, processor 148 may be designed and/orconfigured to perform any method, method step, or sequence of methodsteps in any embodiment described in this disclosure, in any order andwith any degree of repetition. For instance, processor 148 may beconfigured to perform a single step or sequence recurrently until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,assembling inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Processor 148 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1, processor 148 is configured tocalculate a degree of transmission threat 152 between a user 108 and acurrently proximate subject 116. A “degree of transmission threat,” asused in this disclosure, is an output identifying the likelihood of apotential negative occurrence and/or event occurring to a user 108 by acurrently proximate subject 116 and/or a location where the user 108 maybe located. A degree of transmission threat may include but is notlimited to a threat of and/or from transmission of a pathogen ofpossible contagion. A degree of transmission threat may include anindication as to a rate of transmission of a pathogen of possiblecontagion. A potential negative occurrence may include a likelihood ofcontracting a pathogen of possible contagion 156. A degree oftransmission threat may include and/or reflect a potency of a pathogenof possible contagion, mode of transmission including airborne,waterborne, human to human and the like, as well as localized density,personal susceptibility including age, comorbidities, inoculationstatus, antibody status, blood type, and the like. A degree oftransmission threat may be a dynamic value that may be continuallyassessed, stored, calculated, and/or collected for trend analysis. A“pathogen of possible contagion,” as used in this disclosure, is acausative agent of disease. A causative agent of disease includes anydeviation from the normal structural or functional state of a humansubject. A causative agent of disease may be caused by an externalfactor including a pathogen, an internal dysfunction, and the like. Acausative agent of disease may be identified by reviewing a knownpathogen list, which may contain a list of one or more pathogens ofpossible contagion to check for. Information pertaining to a pathogenlist may be stored within pathogen database, as described below in moredetail. In an embodiment, a degree of transmission threat 152 mayreflect information for a user 108 such as where is a safe place totravel to currently, what places that a user traveled to recently orhistorically may impact current behavior, and/or what is the safestand/or best overall location for the user 108 to be located presently.

With continued reference to FIG. 1, a pathogen may include an infectiousmicroorganism and/or agent including but not limited to an alga, abacterium, a fungus, a prion, a viroid, a virus, pollen, and the like. Apathogen may include an infectious microorganism that may be spreadthrough the air, through human to human transmission, through animal tohuman transmission, through a water supply, through the environment,through surface contamination, and the like. A virus may include aninfectious agent that replicates inside living cells of an organism. Avirus may infect all types of life forms and host cells, including butnot limited to animals, plants, microorganisms, bacteria, archaea, humanbeings, and the like. A virus may cause a host cell to produce thousandsof copies of the virus. A virus may be transmitted in numerous ways,including but not limiting by plants, animals, coughing, sneezing, fecaloral routes, hand to mouth contact, food, water, and the like. A virusmay exist as a plurality of shapes, sizes, and morphologies, includingbut not limited to helical viruses, icosahedral viruses, prolateviruses, envelope viruses, complex viruses, giant viruses, and the like.A virus may be comprised of a piece of genetic material, such asdeoxyribonucleic acid (DNA), or ribonucleic acid (RNA) enclosed in acoat of protein. A virus may invade a host cell such as in a user 108and use components of the host cell to replicate and multiply. A virusmay cause an illness or health condition. For instance and withoutlimitation, a virus may include adeno-associated virus, aichi virus,Australian bat lyssavirus, BK polyomavirus, Banna virus, Barmah forestvirus, Bunyamwera virus, Bunyavirus La Crosse, Cercopithecineherpesvirus, Chandipura virus, Chikungunya virus, Coronavirus, CosavirusA, Cowpox virus, Cozsackievirus, Crimean-Congo hemorrhagic fever virus,Dengue virus, Dhori virus, Dugbe virus, Duvenhage virus, Eastern equineencephalitis virus, Ebolavirus, Echovirus, Encephalomyocarditis virus,Epstein-Barr virus, European bat lyssavirus, GB virus C/Hepatitis Gvirus, Hantaan virus, Hendra virus, Hepatitis A virus, Hepatitis Bvirus, Hepatitis C virus, Hepatitis E virus, Hepatitis delta virus,Horsepox virus, Human adenovirus, Human astrovirus, Human coronavirus,Human cytomegalovirus, Human enterovirus, Human herpesvirus, Humanimmunodeficiency virus, Human papillomavirus, Human parainfluenza, Humanparvovirus, Human respiratory syncytial virus, Human rhinovirus, HumanSARS coronavirus, Human spumaretrovirus, Human T-lymphotrophic virus,Human torovirus, Influenza A virus, Influenza B virus, Influenza Cvirus, Isfahan virus, JC polyomavirus, Japanese encephalitis virus,Junin arenavirus, KI polyomavirus, Kunjin virus, Lagos bat virus, LakeVictoria Marburgvirus, Langat virus, Lassa virus, Lordsdale virus,Louping ill virus, Lymphocytic choriomeningitis virus, Machupo virus,Mayaro virus, MERS coronavirus, Measles virus, Mengoencephalomyocarditis virus, Merkel cell polyomavirus, Mokola virus,Molluscum contagiosum virus, Monkeypox virus, Mumps virus, Murray valleyencephalitis virus, New York virus, Nipah virus, Norwalk virus,O′nyong-nyong virus, Orf virus, Oropouche virus, Pichinde virus,Poliovirus, Punta toro phlebovirus, Puumala virus, Rabies virus, Riftvalley fever virus, Rotavirus A, Ross river virus, Rotavirus A,Rotavirus B, Rotavirus C, Rubella virus, Sagiyama virus, Salivirus A,Sandfly fever sicilian virus, Sapporo virus, SARS Coronavirus 2, Semikiforest virus, Seoul virus, Simian foamy virus, Simian virus, Sindbisvirus, Southampton virus, St. Louis Encephalitis virus, Tick-bornepowasan virus, Torque teno virus, Toscana virus, Uukuniemi virus,Vaccinia virus, Varicella-zoster virus, Variola virus, Venezuelan equineencephalitis virus, Vesicular stomatitis virus, Western equineencephalitis virus, WU polyomavirus, West Nile virus, Yaba monkey tumorvirus, Yaba-like disease virus, Yellow fever virus, Zika virus, and thelike.

With continued reference to FIG. 1, bacteria may include a type ofbiological cell. Bacteria may include single celled organisms. Bacteriamay contain a tail, known as a flagellum which may aid in movement.Bacteria may include spherical bacteria such as cocci, rod-shapedbacteria such as bacilli, and/or spiral bacteria such as Spirilla. Abacterium may include one or more components, including but not limitedto a capsule, cell wall, plasma membrane, cytoplasm, DNA, RNA,ribosomes, flagellum, pill, and the like. Bacteria may includeprokaryotic microorganisms that may be present in different shapesincluding for example spheres, rods, and spirals. Bacteria may includefor example Neisseria Meningitides, Streptococcus pneumoniae, Vibriocholerae and Hemophilus influenzae type b. Bacteria may exist as singlecells as well as in certain characteristic patterns. For example,Streptococcus may form chains, and Staphylococcus may group together inclusters. Bacteria may attach to surfaces and form dense aggregationsknown as biofilms. Biofilms may contain multiple species of bacteria,protists, and archaea. Bacteria may include both gram positive bacteriaand gram-negative bacteria. Gram positive bacteria include all bacteriathat take up crystal violet stain used in a gram test, and which thenappear to be purple colored when seen through a microscope. Gramnegative bacteria cannot retain the crystal violet stain as theirpeptidoglycan layer is much thinner as compared to gram positivebacteria, making the cell wall more porous and incapable of retainingthe crystal violet stain. Gram positive bacteria may aid inclassification and subdivision of bacteria and may include certainspecies including but not limited to bacilli, cocci, Staphylococcuscatalase positive, and Streptococcus catalase negative. Gram negativebacteria may include for example, Escherichia coli, Salmonella,Shigella, Pseudomonas, Moraxella, Helicobaeisseria gonorrhoease,Neisseria meningitidis, Moraxella catarrhalis, Hemophilus influenzae,Stenotrophomonas, Bdellovibrio, Legionella, Cyanobacteria, Spirochetes,Green sulfur, and the like. Microorganisms may also be categorized bycellular respiration type as either aerobic or anaerobic. Aerobicmicroorganisms may include any organism that can survive and grow in anoxygenated environment. Anaerobic microorganisms may be able to makeenergy without oxygen through either lactic acid or alcoholicfermentation.

With continued reference to FIG. 1, bacteria may include speciesincluding but not limited to Actinomyces israelii, Bacillus anthracia,Bacteroides fragilis, Bordetella pertussis, Borrelia burgdorferi,Borrelia garinii, Borrelia afzelii, Borrelia recurrentis, Brucellaabortus, Brucella canis, Brucella melitensis, Brucella suis,Campylobacter jejuni, Chlamydia penumoniae, Chlamydia trachomatis,Chalmydophila psittaci, Clostridum botulinum, Clostridum difficile,Clostridum perfringens, Clostridium tetani, Corynebacterium diphtheriae,Ehrlichia canis, Ehrlichia chaffeensis, Enterococcus faecalis,Enterococcus faecium, Escherichia coli, Francisella tularensis,Haemophilus influenzae, Helicobacter pylori, Klebsiella pneumoniae,Legionella pneumophila, Leptospira species, Listeria monocytogenes,Mycobacterium leprae, Mycobacterium tuberculosis, Mycoplasma pneumoniae,Neisseria gonorrhoeae, Neisseria meningitidis, Pseudomonas aeruginosa,Nocardia asteroids, Rickettsia, Salmonella, Shigella, Staphyloccocusaureus, Staphylococcus epidermidis, Staphylococcus saprophyticus,Streptococcus agalactiae, Streptococcus pneumoniae, Streptococcuspyogenes, Streptococcus viridans, Treponema pallidum, Vibrio cholerae,Yersinia pestis, Stachybotrys chartarum, and the like.

With continued reference to FIG. 1, a fungus may include a member ofeukaryotic organisms that include but are not limited to yeasts, molds,and mushrooms. A fungus may reproduce by spreading microscopic sporeswhich may be present in the air and soil and may be inhaled or come intocontact with surfaces of a human body such as through the skin. Fungusmay include opportunistic and/or primary fungus. Fungus may cause fungalinfections that may include localized infections that affect only onearea of the body, such as but not limited to the skin, nails, vagina,mouth, sinuses, and the like. Fungus may cause systemic fungalinfections such as an infection that spreads to one or more locations inthe body and may affect the entire body as opposed to one localizedarea. Fungus may include but are not limited to Candidiasis,Cryptococcosis, Aspergillosis, Coccidioidomycosis, Histoplasmosis,Blastomycosis, Pneumocystis pneumonia, Candida auris, C. neoformans, C.gattii, Mucormycosis, Mycetoma, Ringworm, Sporotrichosis,Paracoccidioidomycosis, Talaromycosis, Stachubotrys chartarum,Mycorrhiza, and the like.

With continued reference to FIG. 1, a prion may include an abnormalpathogenic agent that can induce abnormal folding of specific normalcellular proteins found in various locations of the body such as thebrain. A prion may transmit their misfolded shape onto normal variantsof the same protein, and cause disease. A prion may form an abnormalaggregate of proteins such as amyloids, which may accumulate in infectedtissue and cause tissue damage and cell death. A prion may cause diseasesuch as but not limited to Creutzfeldt-Jakob Disease, VariantCreutzfeldt-Jakob Disease, Familial Creutzfeldt-Jakob Disease, Sporadic,Creutzfeldt-Jakob Disease, Gerstmann-Straussler-Scheinker Syndrome,Fatal Familial Insomnia, Kuru, Familial spongiform encephalopathy,Variably protease-sensitive prionopathy, Bovine SpongiformEncephalopathy, Chronic Wasting Disease, Scrapie, Transmissible minkencephalopathy, Feline spongiform encephalopathy, Ungulate spongiformencephalopathy and the like.

With continued reference to FIG. 1, a viroid may include an infectiouspathogen that may be composed of a short strand of single-stranded RNAthat does not contain a protein coating. A viroid may be transmittedmechanically from one cell to another through cellular debris. Viroidfamilies may include Pospiviroidae, and/or Avsunviroidae. For instanceand without limitation, a viroid may include but is not limited toPopsivviroid, Hostuviroid, Cocaviroid, Apscaviroid, Coleviroid,Avsunviroid, Pelamoviroid and the like.

With continued reference to FIG. 1, a causative agent may include afamily of one or more diseases, including but not limited toadenoviridae, coronaviridae, picornaviridae, Herpesviridae,hepadnaviridae, Flaviviridae, retroviridae, Orthomyxoviridae,paramyxoviridae, papovaviridae, polyomavirus, rhabdoviridae,polyomavirus, rhabdoviridae, and/or togaviridae. A causative agent mayinclude one or more diseases and the like.

With continued reference to FIG. 1, processor 148 identifies a pathogenof possible contagion 156, using information contained within pathogendatabase 160. Pathogen database 160 may include any database suitablefor use as user database 136 as described above. Pathogen database 160may include information relating to various risk factors that mayidentify and/or make a particular pathogen of possible contagion 156 tobe likely to be present in a certain location, and/or to be transmittedto a user 108 by a particular currently proximate subject 116. Forinstance and without limitation, a first currently proximate subject 116that has recently traveled to Africa may be at risk for transmitting apathogen of possible contagion 156 such as Yellow Fever, while a secondcurrently proximate subject 116 that has recently traveled to SanFrancisco, Calif. may be at risk for transmitting a pathogen of possiblecontagion 156 such as typhus. Pathogen database 160 may containinformation relating to certain pathogens of possible contagion 156 thatmay be more likely to appear in certain demographics of individualsand/or in certain settings and locations. For example, a bacterialinfection such as meningitis may be more likely to be prevalent inpopulations such as young adults, infants less than one year old, andpeople traveling to Africa. Similarly, a bacterial infection such asmeningitis may also be more likely to be found in crowded livingconditions such as dormitories, boarding schools, and sleep away camp.Processor 148 may utilize information pertaining to a baselinemeasurement to assess landmarks and population density within a room orlocation to assess risk of crowded living conditions, such as if abaseline measurement indicates that a user 108 and/or currentlyproximate subject 116 are located within a crowded cabin at a sleep awaycamp, then a pathogen of possible contagion 156 such as meningitis maybe identified. Processor 148 may also use data relating to a currentlyproximate subject 116 to identify pathogens of possible contagion 156.For instance and without limitation, a currently proximate subject 116who appears to be less than ten years old, may be used to identify apathogen of possible contagion 156 such as respiratory syncytial virus(RSV), while a currently proximate subject 116 who appears to be overage sixty may be used to identify a pathogen of possible contagion 156such as a Staphylococcus aureus infection. Information pertaining to apathogen of possible contagion 156 may be retrievable, accessible,interoperable, reusable and the like.

With continued reference to FIG. 1, processor 148 may utilizeinformation relating to location component 132 to aid in identifying apathogen of possible contagion 156. For instance and without limitation,an outbreak of a disease may be prevalent in one geographical locationand not in another. For example, a pathogen of possible contagion 156such as the bubonic plague may be prevalent in areas of the UnitedStates such as Colorado, New Mexico, Arizona, Nevada, Oregon, Idaho,Utah, and Wyoming, while the bubonic plague may not be prevalent inother areas such as Maine, Vermont, New Hampshire, Massachusetts, andConnecticut. Location component 132 may also be used to identifypotential environmental toxins and/or exposures to a pathogen ofpossible contagion 156. For example, certain locations near contaminatedjob sites or natural asbestos deposits may indicate a great risk ofbeing exposed to a pathogen of possible contagion 156 such as asbestos.In yet another non-limiting example, an outbreak of a disease such asLegionnaire's disease may be attributed to a particular incident in aspecific location, such as the Flint water crisis that occurred inFlint, Mich. and triggered an outbreak of Legionnaire's disease.Pathogen database 160 may be updated in real time, to contain updatedand accurate information relating to pathogens of possible contagion 156that may be probable in certain demographic groups, found in certaingeographical locations, and/or attributed to one or more risk factorsand/or limitations. Such information may be updated in real time, usingany network methodology as described herein. In yet another non-limitingexample, processor 148 may detect a pathogen of possible contagion 156such as conjunctivitis using machine-learning analysis of images of auser's eyes and/or by measuring the degree of hemoglobin absorption invisible light incident to the exterior of the eyeball. In such aninstance, more hemoglobin may indicate more bloodshot eyes.

With continued reference to FIG. 1, processor 148 is configured tolocate a reproduction rate 164 for a pathogen of possible contagion 156.A “reproduction rate,” as used in this disclosure, is a value indicatingan expected number of cases generated by one case in a given population,where all individuals are susceptible to infection. In an embodiment, areproduction rate may indicate a context dependent and/or localizedsusceptibility to infection based on a certain location, environmentalconditions, and/or local population of microorganism present within alocation. A population may include a population of human subjectsproximate to a user 108 in a room, at an event, at a workplace, in acertain geographical location, and the like. A population may include apopulation of pathogens of possible contagion 156 that may be found in acertain area and/or contained within a regional growth medium. Forinstance and without limitation, Mycobacterium tuberculosis may be foundin soil and/or dung. A reproduction rate 164 may indicate a metric thatdescribes the contagiousness and/or transmissibility of pathogen ofpossible contagion 156. In an embodiment, a reproduction rate 164 mayinclude a basic reproduction number for a pathogen of possible contagion156, such as for example an R 0 value. In an embodiment, a reproductionrate 164 may be calculated, taking into account a population state whereno other individuals are infected and/or immunized naturally and/orthrough vaccination. In an embodiment, a reproduction rate 164 may becalculated taking into account a population state where otherindividuals have been infected and/or immunized naturally throughexposure and/or vaccination. A reproduction rate 164 may be calculatedusing one or more factors including but not limited to environmentalconditions, behavior of infected persons, duration of infectivity ofaffected populations, infectiousness of a pathogen of possible contagion156, sociobehavioral factors, environmental factors, and the number ofsusceptible people that an infected person has come into contact with.Information pertaining to reproduction rate 164 and/or one or morefactors utilized to calculate reproduction rate 164 may be stored andcontained within reproduction database 168. Reproduction database 168may be implemented as any data structure suitable for use as userdatabase 136 as described above in more detail. Information containedwithin reproduction database 168 is described in more detail below. Areproduction rate 164 may be output as a numerical score, a characterscore, a range of scores and the like. A reproduction rate 164 mayprovide information relating to how likely a pathogen of possiblecontagion 156 is likely to spread in a given population. For instanceand without limitation, a reproduction rate 164 greater than one mayindicate that a pathogen of possible contagion 156 is able to startspreading in a population, while a reproduction rate 164 less than onemay indicate that a pathogen of possible contagion 156 is not likely tospread in a given population. In an embodiment, a reproduction rate 164may indicate a dynamic value that may vary based on a particularlocation. Information may be updated in real time using any networkmethodology as described herein.

With continued reference to FIG. 1, reproduction rate 164 may includeand/or be based in part on data provided by a federal, state, municipal,or other government agency such as the Centers for Disease Control (CDC)and/or by a corporation, non-governmental organization, or the like.Alternatively or additionally, reproduction rate 164 may be calculatedusing a reproduction machine-learning model. A “machine-learning model,”as used in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process. A “machine-learningprocess,” as used in this disclosure, is a process that automatedly usestraining data to generate an algorithm that will be performed bywearable device 104 and/or processor 148 to produce outputs such asreproduction rate 164, using inputs such as any factors that may be usedto calculate reproduction rate 164 that may be found within reproductiondatabase 168. This is in contrast to a non-machine-learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. Wearable device 104 mayinclude a machine-learning component 172. A “machine-learningcomponent,” as used in this disclosure, is a component that calculates,generates, and/or performs one or more machine-learning processes.Processor 148 may retrieve a reproduction machine-learning model as afunction of an identified pathogen of possible contagion 156. In anembodiment, one or more reproduction machine-learning models may bestored within reproduction database 168. In an embodiment, processor 148may generate a query to locate a reproduction machine-learning modelrelating to an identified pathogen of possible contagion 156. A “query,”as used in this disclosure, is a datum used to retrieve a reproductionmachine-learning model from a data structure, database, and/or modelsuch as from reproduction database 168. For instance and withoutlimitation, a pathogen of possible contagion 156 such as influenza maybe utilized to generate a query such as “influenza” or “viralinfections.” Reproduction machine-learning model may be trained usingtraining data that may be generated using expert inputs, journalarticles and publications, previous iterations of generating pathogenmachine-learning model and the like. Reproduction machine-learning modelmay utilize one or more factors that impact reproduction rate 164 of apathogen of possible contagion 156 as an input and output a reproductionrate 164. One or more factors that impact reproduction rate 164 may bestored within reproduction database 168 as described above. In anembodiment, one or more pathogens of possible contagion 156 may berelated to a reproduction machine-learning model. For instance andwithout limitation, one or more viruses from a family of viruses may belinked to the same reproduction machine-learning model, whereby viruseswhich are members of the family of coronaviruses including COVID-19,Middle East Respiratory Syndrome (MERS), and Severe Acute RespiratorySyndrome (SARS) may all be queried to a single reproductionmachine-learning model relating to coronaviruses. In yet anothernon-limiting example, one or more bacterial infections from a family ofbacteria may be linked to the same reproduction machine-learning model,whereby for example, species such as S. argenteus, S. arlettae, and S.aureus may all be queried to a single reproduction machine-learningmodel relating to staphylococci. In yet another non-limiting example,one or more family of pathogens may query to different reproductionmachine-learning models. For instance and without limitation, a pathogenof possible contagion 156 such as Candida albicans may be queried to afirst reproduction machine-learning model, where cutaneous candidiasismay be queried to a second reproduction machine-learning model eventhough both Candida albicans and cutaneous candidiasis both belong tothe same family of fungi. Information pertaining to which species andwhich pathogens of possible contagion 156 may be used to identifyparticular reproduction machine-learning models may be selected usingexpert input, including leading scientists, researchers, medicalprofessionals, journal articles, publications, and the like. Processor148 outputs a reproduction rate as a function of a reproductionmachine-learning model, this may be performed using any of themethodologies as described below in more detail.

With continued reference to FIG. 1, processor 148 is configured tocalculate a degree of transmission threat 152 as a function of datainput component 144 and reproduction rate 164. A degree of transmissionthreat includes any of the degree of transmission threats as describedabove in more detail. Processor 148 may calculate a degree oftransmission threat 152, using information relating to a user from userdatabase 136. Processor 148 may retrieve an element of user data, whichmay be stored within user database 136. An element of user data mayinclude information pertaining to user demographic information such as auser's age, gender, occupation, home address, marital status, familystatus, home living arrangements and the like. For example, user datamay indicate that a user 108 is a thirty-six-year-old female who livesat home with the user's spouse, and the user's sixty-six-year-oldmother. Such information may be utilized by processor 148 to account forcontemporaneous factors such as socioeconomic factors, behavioralfactors, and/or environmental factors that may affect and/or impact adegree of transmission threat 152. For example, a user 108 who has anoccupation as a kindergarten teacher, may have a stronger immune systemfrom being around young children all day, which may impact a degree oftransmission threat 152, by making the user less susceptible to certainillnesses or diseases. In yet another non-limiting example, a user 108who has a substance abuse disorder may be at greater risk for developinga disease such as hepatitis, while a user 108 who is unemployed andhomeless may be at risk for developing a disease such as acutenonspecific respiratory diseases and tuberculosis. An element of userdata may also relate to other behaviors and/or socioeconomic factorsthat attribute to rates and/or risk of disease, including any medicalconditions the user may be diagnosed with, immunizations the user mayhave received, titer levels indicating antibody levels and immunity,diseases a user may have been exposed to and the like. For instance andwithout limitation, user data may indicate that a user received animmunization measles, however the user's titer level may indicate thatthe user does not have certain specified levels of antibodies needed tofight off a measles infection should the user become exposed. In such aninstance, a user may require a subsequent dose, round, and/or booster ofcertain immunizations. A titer may also be utilized to evaluate anddetermine possible pathogens that a user may have previously beenexposed to and developed immunity to later infections. For instance andwithout limitation, a user 108 who developed mumps as a child may have atiter that shows the user 108 has life-long immunity as a result ofdeveloping the illness as a child. User data may also indicate whatdiseases a user may be a risk of developing at some point in the future,such as a user who is exposed to varicella-zoster virus as a child, maybe at risk of developing herpes zoster later in life as a result ofdeveloping varicella-zoster virus. User data may include lab data and/orlab results pertaining to a user 108 and/or a currently proximatesubject 116.

With continued reference to FIG. 1, wearable device 104 mayauto-populate information about a user 108, to be utilized whencalculating a degree of transmission threat 152. For instance andwithout limitation, wearable device 104 may auto-populate a user'svaccination and/or inoculation records from a central vaccine database,such as one that may be maintained by a government agency and stored inan encrypted manner. In an embodiment, wearable device 104 mayauthenticate a user's 108 identify before accessing a user's vaccinerecords, such as by using authentication component as described in moredetail below. In yet another non-limiting example, wearable device 104may authenticate and access medical records of a user 108 and/or acurrently proximate subject 116 to ascertain previous medical conditionsa user 108 may have been diagnosed with, and/or other illness a user 108may have been exposed to and recorded from, to indicate some degree ofimmunity that may protect the user 108 from subsequent exposures. In anembodiment, a user 108 and/or a currently proximate subject 116 mayenter own immunization and/or medical history data, such as by usinggraphical user interface as described below in more detail. For example,wearable device 104 may prompt a user 108 for information and may ask auser a series of questions such as what medical conditions a user hasbeen previously and/or currently diagnosed with, or what illness did auser have during childhood. For instance and without limitation,wearable device 104 may list a series of illnesses such as croup,varicella, and pediculus capitis, and ask the user to select anyillnesses that the user previous had and/or acquired. In an embodiment,information contained within a central vaccine database may be encryptedand access to the central vaccine database may be limited to certainaccess and/or viewing rights. For example, access rights to informationcontained within a central vaccine database may be determined byinoculation status, local restrictions such as quarantine rules, testingstatus, state based rules and laws, municipality based rules and laws,and/or business based rules and laws. Access may also be based on userinput and/or what a user feels comfortable allowing other individuals toaccess.

With continued reference to FIG. 1, processor 148 is configured tocalculate a degree of transmission threat 152 of a location where a user108 is located. A degree of transmission threat 152 of a location, mayindicate and/or signal to a user an overall degree of transmissionthreat if a user 108 is to enter a location and/or remain there, basedon the location itself and/or one or more currently proximate subjects116 that may be located at a particular location. For instance andwithout limitation, a degree of transmission threat 152 of a locationmay indicate that a location poses a high risk of threat to a user 108for transmitting a pathogen of possible contagion 156 to the user 108,because of the number of currently proximate subjects 116 located at thelocation, and a cumulative effect of the risk of the number of currentlyproximate subjects 116 present at the location. This information may behelpful such as when the first currently proximate subject 116 that auser comes into contact with at the location has a lower overall degreeof transmission threat 152. In an embodiment, processor 148 may utilizeinformation obtained using location component 132, baseline locationmeasurements, landmark information, and the like to calculate the degreeof transmission threat 152 for a location.

With continued reference to FIG. 1, degree of transmission threat 152may be calculated using fuzzy membership sets. A “fuzzy membership set,”as used in this disclosure, is a classification scheme that assignsmembers to a particular class using probabilities of being a member of aspecified set, as compared to being classified as either belonging to aclass or not belonging to a class. A fuzzy membership set may includebut is not limited to fuzzy gaussian membership, fuzzy large membership,fuzzy linear membership, fuzzy MS large membership, fuzzy MS smallmembership, fuzzy near membership, fuzzy small membership, and the like.For instance and without limitation, a degree of transmission threat 152calculated using a fuzzy membership set may reflect a range of valuesfor a degree of transmission threat 152 that may indicate a specifiedthreat level, as compared to outputting a single degree of transmissionthreat 152. For instance and without limitation, a degree oftransmission threat 152 calculated using a fuzzy membership set mayreflect that a first user 108 having a 10-15% degree of transmissionthreat 152 of contracting rhinovirus from a currently proximate subject116, may be classified as being low risk, while a second user 108 havinga 14-24% degree of transmission threat 152 of contracting rhinovirusfrom a currently proximate subject 116 may be classified as beinglow-medium risk. In an embodiment, wearable device 104 may be configuredto use a fuzzy membership set for locating a pathogen of possiblecontagion 156 as well.

With continued reference to FIG. 1, information contained within userdatabase 116 may be used to authenticate the identity of a user, usingauthentication component 176. An “authentication component,” as used inthis disclosure, is configured authenticate the identity of a user 108at timed intervals, while the user 108 is in possession of the wearabledevice 104. In an embodiment, the identity of a user 108 may be verifiedby receiving, capturing, and/or authenticating biometric data, which maybe referred to herein interchangeably as “biometrics,” by detecting,measuring, or otherwise capturing one or more physiological, behavioral,or biological patterns, qualities, or characteristics identifying aparticular person; identification may be unique, or may be effectivelyunique by, for instance, being highly improbable to be replicated bycapturing biometrics of a different persons. Physiological qualities mayrefer to something that a user is, while behavioral qualities may referto something that a user can do. Wearable device 104 may include acomponent connected and/or paired to wearable device 104, via a wired orwireless connection, capable of taking, processing, and analyzingbiometric authentication of a user 108. Wearable device 104 may includeone or more components of hardware and/or software program code forreceiving, obtaining, and authenticating a biometric signature of a user108. Biometric signature may be generated from biometrics using sensor124 and/or camera 128. Sensor 124 may scan, read, analyze, and/orotherwise obtain a biometric signature produced from a bodily feature ofa user 108. Bodily feature may include a face, a finger, a thumb, aneye, an iris, a retina, a blood composition, a skin or tissue, and thelike. Sensor 124 and/or camera 128 may include an optical scanner whichmay rely on capturing an optical image such as a photograph to capture abodily feature of a user 108. Sensor 124 and/or camera 128 may includecapacitive scanners which may use capacitor circuits to capture a bodilyfeature of a user 108. A capacitive scanner may include an array ofcapacitive proximity sensors connected to wearable device 104 andelectronic signal processing circuits to detect a bodily feature of auser 108. Ultrasonic scanners may use high frequently sound waves todetect a bodily feature of a user 108. Ultrasonic scanners may includean ultrasonic transmitter and receiver. In an embodiment, an ultrasonicpulse may be transmitted over whenever stress is applied so that some ofthe pulse is absorbed and some is reflected back to a sensor that maydetect stress. Intensity of returning ultrasonic pulse at differentpoints on the scanner may result in capturing a particular bodilyfeature of a user 108. In an embodiment, a biometric signature of a user108 may be used to decrypt an encrypted private key, encrypted datarecord, digital signature, or other cryptographically secured orgenerated identity token associated with a user 108. In an embodiment,authentication component 176 may authenticate and identify a user 108and/or a currently proximate subject 116 to aid in ensuring accuratesecurity measures. In an embodiment, authentication component 176 suchas a biometric authentication of a user 108 may be utilized toauthenticate a user's identify before information may be retrieved fromwithin a database and/or that may be stored pertaining to a user, and/orhistorical interactions between a user and wearable device 104.

With continued reference to FIG. 1, processor 148 may be configured tocalculate degree of transmission threat 152 using a machine-learningprocess. A machine-learning process includes any of the machine-learningprocesses as described above in more detail. Calculating amachine-learning process may include selecting a pathogenmachine-learning model as a function of a pathogen of contagion 156. Apathogen machine-learning model may be implemented as anymachine-learning model suitable for use as reproduction machine-learningmodel as described above in more detail. Pathogen machine-learning modelmay be trained using training data that may be generated using expertinputs, journal articles and publications, previous iterations ofgenerating pathogen machine-learning model and the like. Pathogenmachine-learning model may utilize reproduction rate 164 for a pathogenof possible contagion 156 and data relating to a currently proximatesubject as an input and output a degree of transmission threat 152. Oneor more pathogen machine-learning models may be stored and containedwithin pathogen database 160. Pathogen machine-learning model Processor148 may select a pathogen machine-learning model by generating a query,as described above in more detail. For instance and without limitation,a pathogen of possible contagion 156 such as zoonotic influenza may bequeried to a pathogen machine-learning model for influenza, while apathogen of possible contagion 156 such as smallpox may be queried to apathogen machine-learning model for variola virus. Processor 148 mayupload a selected pathogen machine-learning model to wearable device104. Uploading may include receiving the most current and/or updatedversion of pathogen machine-learning model, using internet. This may beperformed using any network methodology as described herein. Processor148 calculates degree of transmission threat 152 using an uploadedpathogen machine-learning model. Machine-learning component 172 may aidin uploading pathogen machine-learning model, and/or calculating degreeof transmission threat 152, as described below in more detail.

With continued reference to FIG. 1, wearable device 104 includes auser-signaling component 180. A “user-signaling component,” as used inthis disclosure, is a component configured to generate an output 184 asa function of a degree of transmission threat 152. An “output,” as usedin this disclosure, is a response generated based on a degree oftransmission threat 152. A response may contain an indication as to acurrently proximate subject 116's overall threat to the future healthand wellbeing of a user 108. A response may indicate on a scale, howdangerous and/or how harmless a currently proximate subject 116 may beto a user. For example, a response may specify that a currentlyproximate subject 116 is of a low overall threat to a user's overallhealth and wellbeing. In yet another non-limiting example, a responsemay specify that a currently proximate subject 116 is of an immediateand high threat to a user 108. A response may contain one or morerecommended actions for a user to take. For example, a response when acurrently proximate subject 116 is of a very high threat and dangerousmay suggest that a user immediately leave the room and to not approachthe currently proximate subject 116. A response may contain arecommended mitigation component. A “mitigation component,” as used inthis disclosure, is a recommendation that may aid in reducing theseverity and/or seriousness of a degree of transmission threat 152. Amitigation component 188 may contain a recommendation for an immediateaction that a user can take to reduce a degree of transmission threat152, such as by leaving the location and putting a maximum amount ofdistance between a user 108 and a currently proximate subject 116. Amitigation component 188 may contain a recommendation for an action thata user can take in the very near future that may help reduce a degree oftransmission threat 152 of a currently proximate subject 116. Forinstance and without limitation, a user may be instructed to immediatelyleave and go home and consume a one time high dose of Vitamin D afterbeing exposed to a currently proximate subject 116 who poses a very highdegree of transmission threat of passing on a pathogen of possiblecontagion such as rhinovirus. A mitigation component 188 may contain arecommendation for a long-term course of action that a user can take toaid in reducing a degree of transmission threat 152. For example, a usermay be instructed to take a course of oseltamivir after being exposed toa currently proximate subject 116 who poses a very high degree oftransmission threat of passing on a pathogen of possible contagion suchas influenza. Wearable device 104 may be configured to identify aprecipitating component relating to a pathogen of possible contagion156. A “precipitating component,” as used in this disclosure, is one ormore identifiable conditions that put a user 108 at increased risk ofacquiring a pathogen of possible contagion 156, and/or a currentlyproximate subject 116 at increased risk of transmitting the pathogen ofpossible contagion 156. An identifiable condition may include certainenvironmental, behavioral, social, and/or external factors that mayrelate to a precipitating component. For instance and withoutlimitation, a currently proximate subject 116 who recently traveled toSouth America, and who did not take prophylactic medication whiletraveling and/or any necessary precautions, may have an identifiablecondition that may put the currently proximate subject 116 at increasedrisk of transmitting locally endemic diseases such as tuberculosis. Inyet another non-limiting example, a user 108 who lives at home with theuser's 108 elderly parents may be at increased risk of transmitting apathogen of possible contagion 156 to the user's 108 parents, includingcommon bacterial infections such as Methicillin resistant Staphylococcusaureus (MRSA) and viral infections such as herpes zoster virus. In yetanother non-limiting example, a user 108 who takes chronicimmunosuppressant medications after having a heart transplant may have aprecipitating component of having an increased risk of catching viraland bacterial infections as compared to the human population who are nottaking chronic immunosuppressant medications. Wearable device 104determines a mitigation component 188 configured to reduce areproduction rate 164 and displays the mitigation component 188. Forinstance and without limitation, a precipitating component such as acold and dry winter that commonly occurs in the Northeast section of theUnited States, make enable viruses such as influenza to more easilyreproduce, replicate, and spread during the winter months. In such aninstance, wearable device 104 may determine a mitigation component 188such as recommending the use of a humidifier and high doses of VitaminD, to aid in reducing the reproduction rate of influenza during thewinter months. Wearable device 104 may display the mitigation component188, such as for example, on graphical user interface 192.

With continued reference to FIG. 1, an output 184 may include a visualoutput. A “visual output,” as used in this disclosure is an output thatcontains words, symbols, numbers, and/or characters as an output. Avisual output may be displayed on a graphical user interface 192.Graphical user interface 192 may include without limitation, a form orother graphical element having display fields, where one or moreelements of information may be displayed. Graphical user interface mayinclude sliders that may be displayed and adjusted based on a givendegree of transmission threat 152. For instance and without limitation,a user with a high degree of transmission threat 152 may have slidersthat contain a visual display illustrating the high degree oftransmission threat 152, while sliders may be scaled back and illustratea lower degree of transmission threat 152 for a low degree oftransmission threat 152. Graphical user interface 192 may include freeform textual entries, where a user 108 may type in follow up questionsor seek to clarify information relating to mitigation components.Graphical user interface 192 may display a series of pictures and/orvisuals displaying an output 184 and/or a degree of transmission threat152. Graphical user interface 192 may display an output 184 and/or adegree of transmission threat 152 as a numerical output that may bebased on a scale and/or continuum. For example, an output 184 mayquantify a degree of transmission threat 152 on a numerical scale, suchas for example a scale from 0 to 100, where an output containing a scoreof 0 may indicate a very low and/or nonexistent degree of transmissionthreat while an output containing a score of 100 may indicate a veryhigh degree of transmission threat. In yet another non-limiting example,a degree of transmission threat 152 may be displayed as a percentagesuch as this currently proximate subject 116 has a 20% risk oftransmitting a pathogen of possible contagion 156 to a user 108. In yetanother non-limiting example, a degree of transmission threat 152 may bedisplayed as a graphical representation, including but not limited to aline graph, a pie chart, a bar chart, a histogram, a line chart, ascatter plot, a funnel chart, a waterfall chart, a pictogram, a graph ofa function, a pictograph, an organizational chart, a flowchart, acosmograph, a dual axis chart, an area chart, a stacked bar graph, amekko chart, a scatter plot chart, a bubble chart, a bullet chart, aheat map and the like. In yet another non-limiting example, graphicaluser interface 192 may contain one or more mitigation components thatmay be useful in conjunction with an output 184. Graphical userinterface 192 may display an output 184 that may contain characterand/or symbolic outputs. For instance and without limitation, an output184 that communicates a low degree of transmission threat may bedisplayed as a happy smiling face emoji, to indicate that a currentlyproximate subject 116 poses very little harm, if any at all to a user108. In yet another non-limiting example, an output 184 thatcommunicates a high degree of transmission threat may be displayed as asad face emoji, to indicate that a currently proximate subject 116 posesa very high threat to a user 108. Graphical user interface 192 maydisplay one or more words and/or warning signs to a user 108. Forinstance and without limitation, graphical user interface 192 maydisplay a message such as “warning do not approach” when a currentlyproximate subject 116 poses a high degree of transmission threat 152,whereas graphical user interface 192 may display a message such as“safe” when a currently proximate subject 116 poses very little if anydegree of transmission threat 152 to a user 108. In an embodiment, acurrently proximate subject 116 who poses a moderate degree oftransmission threat 152 may cause graphical user interface 192 todisplay a moderate risk warning containing one or more mitigationcomponents 188 that may aid in reducing a degree of transmission threat152.

With continued reference to FIG. 1, an output 184 may include a tactileoutput. A “tactile output,” as used in this disclosure, is an outputcontaining a sensory response. A sensory response may include a responsedirected affecting a sense including but not limited to sight, hearing,smell, taste, and/or touch. A tactile output may include for example anaudio alarm that may trigger a certain number of beeps to indicate if auser 108 is near a currently proximate subject 116 who may pose athreat, whereas the audio alarm may not trigger at all if a user 108 isnear a currently proximate subject 116 who may not pose a threat. In yetanother non-limiting example, a tactile output may include a vibration,whereby wearable device 104 may vibrate in a certain manner or a certainnumber of times to indicate that the user is near a currently proximatesubject 116 who may pose harm or risk to a user 108, whereby wearabledevice 104 may not vibrate when a currently proximate subject 116 posesno or minimal risk, or may vibrate in a unique manner based on aparticular degree of transmission threat 152 that a currently proximatesubject 116 may pose. In an embodiment, a vibration may include abuzzing, whereby a particular sequence of buzzing may indicate risk of apotential deadly pathogen of possible contagion. For example, a seriesof three rounds of buzzing may indicate a high risk of coming intocontact with a pathogen of possible contagion such as COVID-19, whereasa series of one round of buzzing may indicate a low and/or nonexistentrisk of coming into contact with COVID-19. In an embodiment, wearabledevice 104 may be programmed to set off a certain series of buzzingand/or vibrations for certain pathogens of possible contagion 156 thatmay be highly prevalent in a particular area, and/or of great concern toa user 108. In an embodiment, an output may include specific alarmsand/or signals to a user 108, such as when the user 108 may come withina certain distance of a currently proximate subject 116, such as within3 feet, 6 feet, 12 feet and the like. In an embodiment, an output may beprogrammed to alert a user 108 when the user 108 comes within a certaindistance of the currently proximate subject, as predetermined based on apathogen of possible contagion 156 and/or a user's preference.

With continued reference to FIG. 1, an output 184 may identify aprotected location in relation to a user 108. A “protected location,” asused in this disclosure, is an area that is considered safe, and where acurrently proximate subject poses a reduced and/or very minimal, if anyat all degree of transmission threat. A protected location may belocated within a location. A protected location may indicate arelatively lower degree of transmission threat, such as a minimal degreeof transmission threat for a location and may indicate the best optionavailable for a user 108, if the user 108 is not able to immediatelyvacate the location. A protected location may be extended to a bestpath, a path to a best location, a best path to best location and thelike. For instance and without limitation, if a user 108 and a currentlyproximate subject 116 are both located at an outdoor Olympic sizeswimming pool, a protected location may be identified where the user 108is located at the first end of the Olympic size swimming pool, and thecurrently proximate subject is located at the second end of the Olympicsize swimming pool. Signaling component 180 and/or wearable device 104may identify protected locations using information contained frombaseline location measurements and/or landmarks. For instance andwithout limitation, signaling component 180 may identify from a baselinelocation measurement that a location where a user 108 and a currentlyproximate subject 116 are located contains several landmarks that can beused to divide the location up into six zones where each zone willmeasure approximately six feet and maintain enough distance to ensuresix people can be comfortably and safely located within the location. Inan embodiment, an output 184 may identify a path and/or route of where auser 108 should walk, travel, and/or move within a location to get to aprotected location if the user 108 is not at a protected location. Forexample, graphical user interface 192 may display a suggested route thata user should take at a wedding venue to get to a protected locationwhere the user 108 has put enough distance between the user 108 and acurrently proximate subject 116 who poses a moderate degree oftransmission threat 152 to the user 108.

With continued reference to FIG. 1, calculation of a degree oftransmission threat 152 may not be limited to being performed on awearable device. In an embodiment, a degree of transmission threat 152may be displayed on a sign at a public location that may be dynamic, andtransient and displayed to patrons who may enter the public location.For instance and without limitation, a degree of transmission threat 152may be calculated for a restaurant, whereby the degree of transmissionthreat 152 may be calculated based on the currently proximate subjects116 located within the restaurant and whereby the degree of transmissionthreat 152 for the restaurant may be displayed to patrons and potentialpatrons who seek to dine at the restaurant but may be unsure of thesafety of the restaurant and may require reassurances. In yet anothernon-limiting example, a degree of transmission threat 152 may becalculated and displayed to convey information to one or more members ofthe public who may be entering and/or leaving a public location,including but not limited to a hotel, a shopping mall, a playground, apark, a zoo, a theater, a store entrance, a fair, and the like.

Referring now to FIG. 2, an exemplary embodiment 200 of user database136 is illustrated. User database 136 may be implemented as any datastructure as described above in more detail in reference to FIG. 1. Oneor more tables contained within user database 136 may includedemographic table 204; demographic table 204 may contain informationrelating to demographic information about a user. For instance andwithout limitation, demographic table 204 may contain informationrelating to a user's full legal name, date of birth, gender, maritalstatus, income, composition of family household, religion, ethnicity,and the like. One or more tables contained within user database 136 mayinclude medical condition table 208; medical condition table 208 maycontain information relating to current medical conditions that a usermay be currently diagnosed with. For instance and without limitation,medical condition table 208 may specify that a user suffers fromhypothyroidism and ulcerative colitis. One or more tables containedwithin user database 136 may include immunization table 212;immunization table 212 may include information relating to anyimmunizations that a user may have received, and on what datesspecifically. For instance and without limitation, immunization table212 may indicate that a user was most recently administered a tetanusbooster three years previously. One or more tables contained within userdatabase 136 may include titer table 216; titer table 216 may includeinformation relating to the titer status of one or more antibodies. Forinstance and without limitation, titer table 216 may indicate that auser has a positive titer status to Hepatitis B, but that the user has anegative titer status to mumps. One or more tables contained within userdatabase 136 may include exposure table 220; exposure table 220 mayinclude information relating to which diseases such as viruses, fungi,viroids, prions, bacteria, and/or viroids that a user may have beenexposed to. For instance and without limitation, exposure table 220 mayindicate that a user has previously been exposed to influenza A andcoronavirus. One or more tables contained within user database 136 mayinclude tracing table 224; tracing table 224 may include informationrelating to other individuals and/or currently proximate subjects that auser may have come into contact with. For instance and withoutlimitation, tracing table 224 may contain information describing allencounters a user has had with other individuals and/or currentlyproximate subjects over a specified time period.

Referring now to FIG. 3, an exemplary embodiment 300 of currentlyproximate subject database 140 is illustrated. Currently proximatesubject database 140 may be implemented as any data structure asdescribed above in more detail in reference to FIG. 1. One or moretables contained within currently proximate subject database 140 mayinclude encounter table 304; encounter table 304 may include informationrelating to encounters that a currently proximate subject may with havewith a user. For instance and without limitation, encounter table 304may contain information detailing the length of an encounter between auser and a currently proximate subject at a bakery when ordering coffee.One or more tables contained within currently proximate subject database140 may include physiological measurement table 308; physiologicalmeasurement table 308 may include one or more physiological measurementscaptured from a currently proximate subject. For instance and withoutlimitation, physiological measurement table 308 may contain ameasurement specifying that a currently proximate subject has atemperature of 97.9 degrees Fahrenheit. One or more tables containedwithin currently proximate subject database 140 may include signal table312; signal table 312 may include information relating to a currentlyproximate subject's signal. For instance and without limitation, signaltable 312 may specify what information a currently proximate subject iswilling to share with a user and/or wearable device 104. One or moretables contained within currently proximate subject database 140 mayinclude location table 316; location table 316 may include informationdescribing the location where a user and a currently proximate subjectinteract. For instance and without limitation, location table 316 mayspecify that a user and a currently proximate subject interacted at anoutdoor wedding in Honolulu, Hi. One or more tables contained withincurrently proximate subject database 140 may include exposure table 320;exposure table 320 may include information relating to anyimmunizations, exposures, and/or titer levels of a currently proximatesubject that may be known, such as from a government immunizationdatabase, publicly available information and the like. For instance andwithout limitation, exposure table 320 may include informationindicating that a currently proximate subject was exposed to H1N1 Swineflu, and the currently proximate subject has a positive titer level tothe H1N1 Swine flu. One or more tables contained within currentlyproximate subject database may include tracing table 324; tracing table324 may include information relating to other encounters a currentlyproximate subject may have had with other currently proximate subjectsand/or other users. For instance and without limitation, tracing table324 may include information describing other currently proximatesubjects and/or users that a currently proximate subject came intocontact with yesterday.

Referring now to FIG. 4, an exemplary embodiment 400 of pathogendatabase 160 is illustrated. One or more tables contained withinpathogen database 160 may include environmental factor table 404;environmental factor table 404 may contain information relating toenvironmental factors that may contribute to prevalence of pathogens ofpossible contagion. For instance and without limitation, environmentalfactor table 404 may include information describing flooding and swampywaters that have attributed to breakouts of West Nile Virus in certainareas of the United States. One or more tables contained within pathogendatabase 160 may include human contact table 408; human contact table408 may include a list of diseases that are transmitted from human tohuman contact. For instance and without limitation, human contact able408 may specify that influenza A may be transmitted from human to humancontact and droplet transmission from coughing, sneezing, and talking.One or more tables contained within pathogen database 160 may includeanimal contact table 412; human contact table 412 may include a list ofdiseases that are transmitted from human to animal contact. For instanceand without limitation, animal contact table 412 may include informationlisting hantavirus as a virus that can be transmitted from human toanimal contact. One or more tables contained within pathogen databasemay include chemical exposure table 416; chemical exposure table 416 mayinclude information detailing chemical exposures and link of suchexposure to disease. For instance and without limitation, chemicalexposure table 416 may specify that exposure to certain chemicals in thewater and/or air conditioning may lead to development of Legionnaire'sdisease. One or more tables contained within pathogen database 160 mayinclude travel table 420; travel table 420 may include one or morepathogens that may be prevalent in a particular area where a user and/ora currently proximate subject may have recently traveled to. Forinstance and without limitation, travel table 420 may specify thatdiseases such as Lyme Disease and Eastern Equine Encephalitis may beprevalent in the North Eastern region of the United States, particularlyin the warmer summer months. One or more tables contained withinpathogen database 160 may include infectivity table 424; infectivitytable 424 may include the information relating to the infectivity of apathogen of possible contagion. For instance and without limitation,infectivity table 424 may specify that a pathogen of possible contagionsuch as Ebola virus is transmitted through contact with bodily fluidssuch as blood.

Referring now to FIG. 5, an exemplary embodiment 500 of reproductiondatabase 168 is illustrated. Reproduction database 168 may beimplemented as any data structure suitable for use as described above inmore detail in reference to FIG. 1. One or more tables contained withinreproduction database 168 may include population density table 504;population density table 504 may include information detailing thepopulation density of one or more locations. For instance and withoutlimitation, population density table 504 may include informationdetailing the population density of a location such as the city ofOrlando, Fla. One or more tables contained within reproduction database168 may include pathogen infectiousness table 508; pathogeninfectiousness table 508 may include information relating to howinfectious a particular pathogen of possible contagion may be. Forinstance and without limitation, pathogen infectiousness table 508 mayspecify that a pathogen of possible contagion such as measles is highlycontagious, whereas a pathogen of possible contagion such as shingles isless contagious. One or more tables contained within reproductiondatabase may include recovery table 512; recovery table 512 may includeinformation relating to the number of people who have recovered from apathogen of possible contagion. For instance and without limitation,recovery table 512 may specify that during the 2019 flu season, 9million Americans recovered from influenza A and influenza B. One ormore tables contained within reproduction database may include deathtable 516; death table 516 may indicate how many human beings have diedas a result of a pathogen of possible contagion over a specified timeperiod. For instance and without limitation, death table 516 may specifythat 9,000 human beings have died during 2020 in Massachusetts fromCOVID-19 infections. One or more tables contained within reproductiondatabase 168 may include behavior adoption table 520; behavior adoptiontable 520 may include information relating to behaviors that may impactthe reproduction rate for a pathogen of possible contagion. For instanceand without limitation, behavior adoption table 520 may specify thathygienic practices such as handwashing can reduce reproduction rates ofrhinovirus by 50%. One or more tables contained within reproductiondatabase 168 may include herd immunity table 524; herd immunity table524 may include information relating to levels of herd immunity thathave been achieved in certain locations for specified pathogens ofpossible contagion. For instance and without limitation, herd immunitytable 524 may indicate that 20% of the population in the state of RhodeIsland has achieved immunity to a pathogen of possible contagion such asparainfluenza.

Referring now to FIG. 6, an exemplary embodiment 600 of machine-learningcomponent 172 is illustrated that may perform one or moremachine-learning processes as described in this disclosure isillustrated. Machine-learning module may perform determinations,classification, and/or analysis steps, methods, processes, or the likeas described in this disclosure using machine-learning processes. Amachine-learning process includes any of the machine-learning processesas described above in more detail in reference to FIGS. 1-6. Amachine-learning process uses training data 604 to generate an algorithmthat will be performed by processor 148 and/or wearable device 104 toproduce outputs 608 given data provided as inputs 612; this is incontrast to a non-machine-learning software program where the commandsto be executed are determined in advance by a user and written in aprograming language.

With continued reference to FIG. 6, training data 604 includes any ofthe training data as described above in more detail in reference toFIG. 1. For instance and without limitation, training data 604 mayinclude a plurality of data entries, each entry representing a set ofdata elements that were recorded, received, and/or generated together;data elements may be correlated by shared existence in a given dataentry, by proximity in a given data entry, or the like. Multiple dataentries in training data 604 may evince one or more trends incorrelations between categories of data elements; for instance, andwithout limitation, a higher value of a first data element belonging toa first category of data element may tend to correlate to a higher valueof a second data element belonging to a second category of data element,indicating a possible proportional or other mathematical relationshiplinking values belonging to the two categories. Multiple categories ofdata elements may be related in training data 604 according to variouscorrelations; correlations may indicate causative and/or predictivelinks between categories of data elements, which may be modeled asrelationships such as mathematical relationships by machine-learningprocesses as described in further detail below. Training data 604 may beformatted and/or organized by categories of data elements, for instanceby associating data elements with one or more descriptors correspondingto categories of data elements. As a non-limiting example, training data604 may include data entered in standardized forms by persons orprocesses, such that entry of a given data element in a given field in aform may be mapped to one or more descriptors of categories. Elements intraining data 604 may be linked to descriptors of categories by tags,tokens, or other data elements; for instance, and without limitation,training data 604 may be provided in fixed-length formats, formatslinking positions of data to categories such as comma-separated value(CSV) formats and/or self-describing formats such as extensible markuplanguage (XML), JavaScript Object Notation (JSON), or the like, enablingprocesses or devices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 6,training data 604 may include one or more elements that are notcategorized; that is, training data 604 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 604 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 604 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 604 used by machine-learning component 172 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample, an input 612 may include a reproduction rate for a pathogen ofpossible contagion 164, and an output 608 may include a degree oftransmission threat 152. In yet another non-limiting example, an input612 may include one or more factors that may affect a reproduction rate164, and an output may include a reproduction rate 164.

Further referring to FIG. 6, training data 604 may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 616. Training data classifier 616 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine-learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning component 172 maygenerate a classifier using a classification algorithm, defined as aprocess whereby a processor and/or any module and/or component operatingthereon derives a classifier from training data 604. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 616 may classify elements of training data 604 tospecific geographic locations where pathogens of possible contagion maybe found and/or more likely to be found, demographic populations ofusers and/or currently proximate subjects, pathogens of possiblecontagion, and the like.

Still referring to FIG. 6, machine-learning component 172 may beconfigured to perform a lazy-learning process 620 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine-learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 604. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 604 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 6,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 624. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 624 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 624 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 604set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 6, machine-learning algorithms may include atleast a supervised machine-learning process 628. At least a supervisedmachine-learning process 628, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude inputs 612 as described above as inputs, and outputs 608 asdescribed above as outputs, and a scoring function representing adesired form of relationship to be detected between inputs and outputs;scoring function may, for instance, seek to maximize the probabilitythat a given input and/or combination of elements inputs is associatedwith a given output to minimize the probability that a given input isnot associated with a given output. Scoring function may be expressed asa risk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided intraining data 604. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various possiblevariations of at least a supervised machine-learning process 628 thatmay be used to determine relation between inputs and outputs. Supervisedmachine-learning processes may include classification algorithms asdefined above. Scoring function may include calculating inoculationstatus, antibodies, security settings, sharing of systems that may bemapped to possible conditions based on prevalence in a local populationand the like.

Further referring to FIG. 6, machine-learning processes may include atleast an unsupervised machine-learning processes 632. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 6, machine-learning component 172 may bedesigned and configured to create a machine-learning model 624 usingtechniques for development of linear regression models. Linearregression models may include ordinary least squares regression, whichaims to minimize the square of the difference between predicted outcomesand actual outcomes according to an appropriate norm for measuring sucha difference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 6, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.Machine-learning algorithms and/or machine-learning processes may beupdated in real-time to reflect and contain real time populationstatistics, outbreaks of pathogens of possible contagion 152,vaccination efficacy, mitigation efforts, and the like.

Referring now to FIGS. 7A-7B, exemplary embodiments 700 of detectioncomponent 112 are illustrated. Referring now to FIG. 7A, detectioncomponent 112 may include any component, device, and/or technology asdescribed above in more detail in reference to FIG. 1. Detectioncomponent 112 may include a sensor 124, and/or a camera 128 as describedabove in more detail. Detection component 112 may be configured todetect baseline location measurement. A baseline location measurementincludes any of the baseline location measurements as described above inmore detail in reference to FIG. 1. For instance and without limitation,a baseline location measurement may detect any furniture, items,currently proximate subjects, landmarks, and the like that may becontained within a specified location, such as a particular room in ahouse or a public space such as an event stadium or movie theater. In anembodiment, a location may include an area that a user 108 is currentlylocated, and/or an area that a user 108 may be located at in the future.For example, a baseline location measurement may be taken of an eventhall where a user will be attending an event later on in the day.Information pertaining to a baseline location measurement may be storedand saved within wearable device 104. In an embodiment, a baselinelocation measurement may detect one or more landmarks contained within alocation. For example, detection component 112 may detect a firstlandmark 704, such as a plant contained within a location, a secondlandmark 708 such as a couch, and a third landmark 712 such as a lamplocated next to the couch. Wearable device 104 may utilize informationpertaining to a landmark to offer suggestions to a user 108 about wherethe user may need to position themselves within a location to bestprotect the user's 108 health and risk of infection. For instance andwithout limitation, a landmark may convey transmission risk based on thematerial, composition, and/or ultraviolet exposure of a landmark. Forexample, the interior of a doorknob may have a different degree ofthreat as compared to the exterior of the doorknob. In yet anothernon-limiting example, a doorknob may have a first degree of threatduring the day when the doorknob is located in direct sunlight, whereasthe doorknob may have a second degree of threat during the evening andat night when the doorknob is not exposed to any sunlight or very lowlevels of sunlight. A landmark may also be used to aid in identifyingprotected locations in relation to a user 108. Referring now to FIG. 7B,detection component 112 may be utilized to detect one or more currentlyproximate subjects 116 that be present in a location and/or located neara user 108. In an embodiment, detection component 112 may detect a firstcurrently proximate subject 716, a pair of second currently proximatesubjects 720, and a third currently proximate subject 724. In anembodiment, wearable device 104 may calculate a degree of transmissionthreat 152 between the user 108 and each currently proximate subject,including a first degree of transmission threat 152 between the user 108and the first currently proximate subject 716, a second degree oftransmission threat 152 between the user 108 and the second currentlyproximate subject 720, and a third degree of transmission threat 152between the user 108 and the third currently proximate subject 724.Wearable device 104 may calculate a degree of transmission threat 152 ofa location where a user 108 is located, such as when there may bemultiple currently proximate subjects 116 located in a particularlocation. For instance and without limitation, degree of transmissionthreat 152 of a location may reflect the overall threat level of alocation for a user. For example, a location that has very poor airventilation, few windows, and very little sunlight may have a higherdegree of transmission threat to a user's health for transmittingpathogens of possible contagion, as compared to a location that hasexcellent air circulation, and plenty of windows and doors that canallow plenty of fresh air to be brought inside the location. In yetanother non-limiting example, a location that is crowded but that hasexcellent air circulation and air conditioning may have a lower degreeof transmission threat to a user's health for transmitting pathogens ofpossible contagion, as compared to a location that is not as crowded bythat has very poor air circulation and outdated air filtration.

Referring now to FIGS. 8A-C, exemplary embodiments 800 of variousoutputs 184 are illustrated. Referring now to FIG. 8A, in an embodiment,wearable device 108 may display on graphical user interface 192, adegree of transmission threat 152, which may specify the degree oftransmission threat of a location. A location may include any locationas described above in more detail in reference to FIGS. 1-7. In anembodiment, an output 184 may include a visual display 804, which mayvisually provide information relating to a degree of transmission threat152. For instance and without limitation, a visual display 804 mayinclude an emoji, that may communicate if a location and/or currentlyproximate subject 116 is safe to be around or not. For example, a happysmiling face emoji may communicate to a user 108 that it is safe to stayin the location. An output 184 may include a tactile output, includingany of the tactile outputs as described above in more detail inreference to FIG. 1. An output 184 may include one or more recommendedmitigation components 188. For instance and without limitation, when adegree of transmission threat 152 of a location is 7%, wearable device104 may display a mitigation component 188, recommending a user 108 toremain 6 feet away from all subjects contained within a location, to aidin reducing risk of transmission of a pathogen of possible contagion156. In an embodiment, a mitigation component 188 may be selected and/orrecommended for a user 108 as a function of a precipitating componentrelating to a pathogen of possible contagion 156. For instance andwithout limitation, a mitigation component 188 may recommend extraprecautions to be taken such as wearing a mask and frequent handwashingif a precipitating component indicates that a pathogen of contagion suchas rotavirus has been prevalent in certain demographics which a user 108appears to fit into. Referring now to FIG. 8B, in an embodiment,wearable device 104 may display a degree of transmission threat 152 of acurrently proximate subject 116. In an embodiment, an output 184 mayinclude an emoji such as a sad face 808 when a currently proximatesubject 116 and/or a location may not be safe for a user. In such aninstance, a mitigation component 188 may recommend a user 108 to leave alocation immediately, because the risk of danger, harm, and/or illnessto a user 108 is too immense. In an embodiment, wearable device 104 maycontinue to generate outputs 184, including both visual and/or hapticoutputs until a user has exited a location where there is a high degreeof transmission threat 152 either to a currently proximate subject 116and/or a location. For example, wearable device 104 may utilize locationcomponent 132 to sense and/or detect where a user 108 is located andcontinue to generate and transmit outputs 184 until a user 108 is nolonger in danger. Referring now to FIG. 8C, in an embodiment, output 184may identify a protected location in relation to a user 108. A protectedlocation includes any of the protected locations as described above inmore detail in reference to FIG. 1. In an embodiment, graphical userinterface 192 may display a map 812, illustrating a route for a user 108to travel to a protected location. For example, map 812 may display aphotograph and/or image of location and show a visible route that a usershould take to get to a protected location. This may include forexample, avoiding currently proximate subjects 116, and/or navigating toand/or around landmarks, using information obtained from a baselinelocation measurement. In an embodiment, directions to a protectedlocation may also be provided by tactile outputs, such as if wearabledevice 104 provides audio and spoken instructions for how a user 108should travel to a protected location. In an embodiment, wearable device104 may vibrate to provide instructions for how a user may seek totravel to a protected location.

Referring now to FIG. 9, an exemplary embodiment 900 of a method ofreducing exposure to pathogens of possible contagion is illustrated. Atstep 905, a wearable device 104 detects a currently proximate subject116 in relation to a user 108, wherein the user 108 is in possession ofthe wearable device 104. Wearable device 104 may include any of thewearable devices as described above in more detail in reference to FIGS.1-8. In an embodiment, wearable device 104 detects a currently proximatesubject 116 in relation to a user 108, using any of the methodologies asdescribed above in more detail in reference to FIGS. 1-8. In anembodiment, were able device 104 may detect a currently proximatesubject 116 using detection component 112, including for example sensor124 and/or camera 128.

With continued reference to FIG. 9, at step 910, wearable device 104captures data relating to a currently proximate subject 116. Datarelating to a currently proximate subject 116 includes any of the dataas described above in more detail in reference to FIGS. 1-8. Data mayinclude information that may be publicly available about a currentlyproximate subject 116, including information such as if the currentlyproximate subject has a cough, has a temperature, has a rosy face, isperspiring heavily, has crackles or labored breathing, and the like.Data may include information that a currently proximate subject 116 mayagree to share with wearable device 104, including an authenticatorwhich may specify what information can be shared about a currentlyproximate subject 116 and the like with a user 108 and/or a wearabledevice. For instance and without limitation, a currently proximatesubject 116 may agree to share information relating to the currentlyproximate subject's 116 immunization records but no other data.

With continued reference to FIG. 9, at step 915, wearable device 104calculates a degree of transmission threat 152 between a user 108 and acurrently proximate subject 116. A degree of transmission threat 152,includes any of the degrees of threat 152 as described above in moredetail in reference to FIGS. 1-8. Wearable device 104 calculates adegree of transmission threat 152 by identifying a pathogen of possiblecontagion 156, locating a reproduction rate 164 for the pathogen ofpossible contagion 156, and calculating a degree of transmission threat152 as a function of data relating to the currently proximate subjectand the reproduction rate 164. This may be performed utilizing any ofthe methodologies as described above in more detail in reference toFIGS. 1-8.

With continued reference to FIG. 9, at step 920, wearable device 104generates an output 184 as a function of a degree of transmission threat152. An output 184 includes any of the outputs as described above inmore detail in reference to FIGS. 1-8. An output 184 may include amessage relating to a degree of transmission threat 152. For instanceand without limitation, an output 184 may recommend a user to leave alocation immediately, because a degree of transmission threat 152 is toohigh. In yet another non-limiting example, an output 184 may include amessage informing a user 108 to avoid coming within more than 3 feet ofa currently proximate subject 116. An output 184 may include a visualdisplay, and/or a tactile output as described above in more detail inreference to FIGS. 1-8. An output 184 may identify a mitigationcomponent that may aid in reducing a reproduction rate 164 of a pathogenof possible contagion 156. This may include any of the mitigationcomponents as described above in more detail in reference to FIGS. 1-8.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 10 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1000 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1000 includes a processor 1004 and a memory1008 that communicate with each other, and with other components, via abus 1012. Bus 1012 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 1008 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1016 (BIOS), including basic routines thathelp to transfer information between elements within computer system1000, such as during start-up, may be stored in memory 1008. Memory 1008may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1020 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1008 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1000 may also include a storage device 1024. Examples ofa storage device (e.g., storage device 1024) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1024 may beconnected to bus 1012 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1024 (or one or more components thereof) may be removably interfacedwith computer system 1000 (e.g., via an external port connector (notshown)). Particularly, storage device 1024 and an associatedmachine-readable medium 1028 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1000. In one example,software 1020 may reside, completely or partially, withinmachine-readable medium 1028. In another example, software 1020 mayreside, completely or partially, within processor 1004.

Computer system 1000 may also include an input device 1032. In oneexample, a user of computer system 1000 may enter commands and/or otherinformation into computer system 1000 via input device 1032. Examples ofan input device 1032 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1032may be interfaced to bus 1012 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1012, and any combinations thereof. Input device 1032may include a touch screen interface that may be a part of or separatefrom display 1036, discussed further below. Input device 1032 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1000 via storage device 1024 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1040. A networkinterface device, such as network interface device 1040, may be utilizedfor connecting computer system 1000 to one or more of a variety ofnetworks, such as network 1044, and one or more remote devices 1048connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1044, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1020, etc.) may be communicated to and/or fromcomputer system 1000 via network interface device 1040.

Computer system 1000 may further include a video display adapter 1052for communicating a displayable image to a display device, such asdisplay device 1036. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1052 and display device 1036 maybe utilized in combination with processor 1004 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1000 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1012 via a peripheral interface 1056.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions, and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. A wearable device for reducing exposure to potential dangers, thewearable device comprising: a detection component configured to: performa baseline location measurement of a location containing a currentlyproximate subject; and detect the currently proximate subject inrelation to a user, wherein the user is in possession of the wearabledevice; a data input component configured to capture data relating tothe currently proximate subject; a processor configured to calculate adegree of transmission threat between the user and the currentlyproximate subject as a function of the data relating to the currentlyproximate subject; and a user-signaling component configured to generatean output as a function of the degree of transmission threat, whereingenerating the output further comprises: identifying a protectedlocation; and navigating to the protected location.
 2. The device ofclaim 1, wherein the detection component further comprises a sensor. 3.The device of claim 2, wherein the sensor is configured to detectharmful chemicals.
 4. The device of claim 1, wherein the user-signalingcomponent is configured to alert the user of potential dangers as afunction of a physiological measurement of the currently proximatesubject.
 5. The device of claim 1, wherein the user-signaling componentis configured to send alerts to a group of users, wherein the group ofusers are in a hyper-localized area.
 6. The device of claim 5, whereinthe group of users include a currently proximate subject that is relatedto the user.
 7. The device of claim 1, wherein the detection inputcomponent further comprises a camera.
 8. The device of claim 1, whereinthe detection component is further configured to detect a volatileorganic compound of the currently proximate subject.
 9. The device ofclaim 1, wherein the detection component is further configured to detecta first currently proximate subject in relation to a second currentlyproximate subject.
 10. The device of claim 1, wherein the currentlyproximate subject is classified as low risk or high risk to the user inpossession of the wearable device.
 11. The device of claim 1, whereinthe pathogen of possible contagion comprises a level of potentialenvironmental toxins.
 12. The device of claim 1, wherein the detectioncomponent is further configured to perform simultaneous localization andmapping.
 13. The device of claim 1, wherein generating an output furtherincludes illustrating a route for the user to travel to the protectedlocation.
 14. The device of claim 11, wherein the route includesavoiding currently proximate subjects.
 15. The device of claim 1,wherein the data input component is further configured to: detect asignal from a device in the possession of the currently proximatesubject; identify an authenticator of the currently proximate subject asa function of the signal; and capture data as a function of theauthenticator of the currently proximate subject.
 16. The device ofclaim 1, wherein locating the reproduction rate further comprises:retrieving a reproduction machine-learning model as a function of theidentified pathogen of possible contagion; and outputting thereproduction rate as a function of the reproduction machine-learningmodel.
 17. The device of claim 1, wherein calculating the degree oftransmission threat further comprises: selecting a pathogenmachine-learning model as a function of the pathogen of contagion;uploading the pathogen machine-learning model to the wearable device;and calculating the degree of transmission threat as a function of theuploaded pathogen machine-learning model.
 18. The device of claim 1,wherein calculating the degree of transmission threat further comprises:retrieving an element of user data; and calculating the degree oftransmission threat as a function of the user data.
 19. The device ofclaim 1, wherein the processor is further configured to calculate adegree of transmission threat of a location wherein the user is located.20. A method of reducing exposure to potential dangers, the methodcomprising: performing, by a wearable device, a baseline locationmeasurement of a location containing a currently proximate subject;detecting, by the wearable device, a currently proximate subject inrelation to a user wherein the user is in possession of the wearabledevice; capturing, by a data input component, data relating to thecurrently proximate subject; calculating, by a processor, a degree oftransmission threat between the user and the currently proximate subjectas a function of the data relating to the currently proximate subject;generating, by a user-signaling component, an output as a function ofthe degree of transmission threat, wherein generating an output furthercomprises: identifying a protected location; and navigating to theprotected location.